AlphaFold: Transforming Biomedical Research in Neurodegeneration, Global Health, and Therapeutics
- Renaldo Pool, BHSc
- Jun 2
- 34 min read
Updated: Jun 6
AlphaFold’s groundbreaking advancements have transformed the study of protein structures, paving the way for deeper insights into disease mechanisms. From unraveling the complexities of Alzheimer’s neurofibrillary tangles to exposing vulnerabilities in malaria parasites, AlphaFold is reshaping drug discovery and vaccine development.
Table of Contents
Introduction
AlphaFold’s ongoing development has elevated the understanding of protein structures, their dynamic environments, and their interactive characteristics. The next step in applying this revolutionary AI tool is to uncover the intricate mysteries surrounding disease mechanisms, which will be examined in the following article.
From highlighting the neurofibrillary tangles involved in Alzheimer’s Disease (AD) to providing deeper insights into the mode of action of malaria parasites and ways to combat them, AlphaFold is involved in real-world scenarios, accelerating drug discovery and vaccine development while addressing challenges linked to neurodegenerative conditions, public health threats, cancer progression, and more. Given all this, one question remains: How will AlphaFold continue to reveal and advance our understanding of biological disease mechanisms, aiding humanity's fight against ongoing health threats?
Key Aspects
AlphaFold’s insights into neurodegenerative conditions profoundly impact understanding various disorder mechanisms through protein aggregate modeling.
In addition to highlighting the structural mechanisms of oncogenes that drive cancer development, AlphaFold is also implicated in revealing proteins from the malaria parasite, thereby enabling the design of targeted drug therapies.
The structural prediction of viral and bacterial modes of action also contributes to identifying druggable targets and future therapeutic approaches.
AlphaFold and its derivative AI tools have clarified drug target identification and drug design, assisting in the future development of personalized therapeutic approaches.
Prospects involve integrating AlphaFold with multimodal techniques and incorporating multiomics, broadening its application in the scientific, medical, and pharmaceutical fields.
Understanding Different Disease Mechanisms
Neurodegenerative Conditions and Other Protein Pathologies
The normal biological process of protein folding involves hydrogen bonding and hydrophobic forces, which contribute to the three-dimensional shape, stabilizing the protein structure. It also affects the precise amino acid chain sequence that conforms to this shape, enabling protein functionality. However, when this structure misfolds due to abnormal shape formation, the protein function is disrupted, thereby altering cellular function (Kuhlman & Bradley, 2019). The misfolded protein structures aggregate, causing the formation of toxic species that are implicated in various diseases (Kuhlman & Bradley, 2019). The following section focuses on AlphaFold’s application in understanding these disease mechanisms.
The knowledge gained from studying the structural insights into protein misfolding and aggregates related to conditions such as AD and Parkinson’s disease (PD) provides crucial information on how dysregulated protein folding disrupts cellular function, impacting synaptic activity, mitochondrial function, and proteostasis. This results in neuronal death by forming toxic oligomers or aggregates (Limbocker et al., 2023).
Although AlphaFold has recently advanced our understanding of the causes and progression of neurodegenerative conditions through protein prediction and machine learning models (Gelir et al., 2025), many areas of neural dysfunction remain largely unexplored. Incorporating AlphaFold in prospective research can allow researchers to concentrate on developing therapeutic methods, such as druggable targets and targeted drug discovery, ultimately speeding up the clinical trial process specifically for these conditions (Gelir et al., 2025).
AlphaFold’s Application in Alzheimer’s Disease
When examining the pathogenesis of AD, the structure predictions for amyloid-beta (Ab) enabled the discovery of regions easily influenced by aggregation. These sections are referred to as amyloidogenic regions. The structural information explains oligomer and fibril toxicity formation, which affects neuronal functionality through synaptic function disruption and initiates oxidative stress (Chuguransky, 2021; Limbocker et al., 2023). Furthermore, tau proteins implicated in AD are characterized by hyperphosphorylation and misfolding structures. Specific mutations or post-translational modifications (PTMs) affect tau’s shape, destabilizing it and leading to aggregation and neurofibrillary tangle formation (Chuguransky, 2021; Outeiro et al., 2025).
Where structural modeling aids in further understanding AD is the tissue-specific mapping of proteins observed in CSF, brain tissue, and plasma. Pathways and functional responses are highlighted, and how these conditions are impacted by neuroinflammation, oxidative stress, and dysregulated synaptic function (Gadde et al., 2024). One study applied AlphaFold 3 to map more than 1200 proteins in the brain, CSF, and plasma. It indicated unique mechanisms affected by cis- and trans-protein trait loci (pQTLs), highlighting groups of proteins with specific structural proteins linked to particular neurological pathways and involved in disease mechanisms (Gadde et al., 2024).
Parkinson’s Disease and AlphaFold
a-Synuclein’s characteristic conformation includes misfolding and aggregation into Lewy bodies, contributing to dysfunction in other proteins such as TMEM175. Applying AlphaFold to future studies can elucidate how environmental factors and mutational changes influence this protein’s structure, such as how it causes alterations leading to toxic aggregates seen in PD (Limbocker et al., 2023; Outeiro et al., 2025).
Moreover, an endolysosomal ion channel, identified as TMEM175, has been implicated in PD’s pathogenesis. Structural predictions highlighted the effect of specific mutations altering its function, lysosomal homeostasis, and neuronal health. Another study used imaging techniques, such as magnetic resonance imaging (MRI), to investigate abnormal microstructural components in the substantia nigra and locus coeruleus areas of the brain to determine early changes in PD; prospective research can combine AlphaFold to elucidate molecular mechanism interactions involved in these changes (Pasquini et al., 2025).
In addition, another protein, PINK1, was modeled. This indicated the effect of mutations on the dimer structure, which is implicated in early-onset PD. Emphasizing possible early detection methods that should be investigated (Google DeepMind, 2022).
Using AlphaFold to Explore Schizophrenia
Schizophrenia is characterized as a complex psychiatric disorder associated with neurostructural and functional abnormalities, where misfolded proteins and abnormal signaling pathways contribute to its pathogenesis (Xu et al., 2025).
Using AlphaFold for the structural modeling of previously discovered biomarkers elucidates their impact on this disorder’s mechanisms. For example, one serum protein (zinc finger protein 729) known to be affected in this condition is used as a biomarker (Xu et al., 2025).
In addition, interleukin-6 (IL-6) is implicated in Schizophrenia, where AlphaFold can be utilized to highlight its role in receptor interaction and interacting with other molecules, contributing to neuroinflammation and disease progression (Domenici et al., 2010). Alongside studies done for brain disruptions in energy metabolism, structural insights into enzymatic involvement in metabolic pathways highlight the changes to the structure of these proteins, resulting in impaired function.
Exploring the Mechanisms of Action of Depression with AlphaFold
Using AlphaFold to provide insight into the interplay of mechanisms impacting chronic depression enables researchers to determine how neuroinflammatory and neurodegenerative processes integrate.
Previous studies have highlighted the potential of IL-6 as a biomarker in patients with depression, among other inflammatory cytokines and molecules involved in oxidative stress (Domenici et al., 2010). Future research could focus on AlphaFold’s capability to depict the structures of proteins related to these processes and how their shape alterations affect the function and coincide with typical depressive symptoms. Additionally, AlphaFold’s structural predictions offer researchers insights into the interactions of depression-related proteins within complex networks, disrupting molecular pathways in chronic depression. For instance, a study by Schulze et al. (2025) linked COVID-19-related depression with differentially expressed genes and hub proteins (e.g., MPO, CD163); future studies might incorporate AlphaFold’s structural predictions to illuminate structural vulnerabilities.
The Implications of AlphaFold in Stroke Occurrence
Another condition where AlphaFold has provided insight is the modeling of proteins involved in stroke occurrence by studying the protein’s role in cerebrovascular integrity and post-stroke recovery. Tissue-specific protein involvement in plasma and CSF also indicated the affected pathways in stroke-related neurodegeneration (Gadde et al., 2024).
Huntington’s Disease and AlphaFold’s Insights
Although not extensively studied, AlphaFold has also been used to elucidate the structural components of the huntingtin protein associated with Huntington’s disease (Brotzakis et al., 2025). This, in turn, can also reveal the impact of protein misfolding and toxic aggregation formation on neuronal function (Limbocker et al., 2023).
Other Proteinopathies
AlphaFold’s application extends to other proteinopathies, such as prion diseases, characterized as easily transmitted spongiform encephalopathies (Limbocker et al., 2023; Zheng et al., 2024). The structural insights gain traction when the prion proteins change their shape from typical a-helical structures to a pathogenic b-sheet-rich conformation; AlphaFold enables insight into how these proteins cause neurodegeneration by modeling these shape alterations, encouraging aggregation and distribution (Brotzakis et al., 2025). These insights have been reviewed by Zheng et al. (2024), where mutation V210I present in the alpha-2 and alpha-3 inter-helical structure affects the structure shape and the stability of the protein.
Understanding the Mechanisms of Toxicity through AlphaFold’s Application
The short-lived oligomeric stages of proteins pose a challenge to understand through experimental research; however, with AlphaFold’s capabilities, it proves possible (Yang et al., 2023). Elucidating oligomer toxicity enables understanding how it impacts membrane integrity and cellular processes.
This is likewise seen with misfolded proteins impacting other cellular components through abnormal interactions, worsening a toxic state. AlphaFold depicts these abnormal protein interactions, for instance, tau binding to microtubules or α-synuclein with mitochondrial membranes, influencing its associated conditions (Outeiro et al., 2025).
AlphaFold’s Utilization for Cross-Disease Insights
Another component of AlphaFold is that it aids in predicting an array of protein structures, enabling comparisons where proteins associated with neurodegenerative conditions share overlapping mechanisms of misfolding and aggregation (Li et al., 2021; Outeiro et al., 2025).
Condition | Misfolded Protein | Mechanism of Action |
Alzheimer’s Disease | Amyloid-beta (Aβ), Tau | Aβ aggregates form plaques; hyperphosphorylated tau forms neurofibrillary tangles, disrupting neuronal communication. |
Parkinson’s Disease | α-synuclein | Aggregates into Lewy bodies, impairing mitochondrial function and synaptic transmission. |
Prion Diseases | Prion Protein (PrP) | Misfolded PrP converts normal PrP into toxic β-sheet-rich forms, propagating neurodegeneration. |
Huntington’s Disease | Huntingtin (polyglutamine) | Expanded polyQ repeats cause aggregation, leading to neuronal toxicity. |
Table 1: Neurodegenerative Conditions and Their Mechanisms of Action
Protein Folding and Its Implications for Other Conditions
Malaria – aiding in prevention and treatment
AlphaFold’s protein structure modeling software extends to understanding the main protein components implicated in parasitology-associated infections. One prime example is Plasmodium falciparum, one of the main organisms linked to causing malaria (Abugri et al., 2022).
Global Parasitic Burden
According to WHO reports, the persistent global issue of malaria infections revealed 263 million cases in 2023, increasing from 252 million in 2022. The global incidence per case 2022 indicated that 58 per 1000 individuals were at risk, higher than pre-COVID-19 pandemic levels, and did not meet the Global Technical Strategy for Malaria targets. Five hundred ninety-seven thousand individuals passed away from malaria infections in 2023, with 94% of cases predominantly in the African region. In addition, other regions, such as South Asia, Southeast Asia, India, and Latin America, are known to be endemic malaria areas (Yan et al., 2022).
The main challenges for malaria prevention include climate change influencing vector behavior and resistance to antimalarial drugs. Recently, vaccine rollouts (RTS,S/AS01) in countries such as Ghana, Kenya, and Malawi have reduced the severity of cases and childhood deaths by 13%, with the WHO recommending an additional vaccine (R21/Matrix-M) to be made available in countries considered high-burden areas.
AlphaFold’s Impact on Plasmodium Species and Beyond
The leaps and bounds that AlphaFold has made thus far toward understanding malaria’s mode of action are highlighted by the structure predictions for proteins encoded by previously unknown genes (Behrens & Spielmann, 2024). The prediction models were compared to experimentally tested structures, revealing similar structures in 353 proteins whose functions are yet to be discovered. This step decreased the number of unidentified proteins by 25% (Behrens & Spielmann, 2024). It also identified that although several parasite proteins are altered due to evolutionary changes, certain conserved regions of these structures remain for functional purposes (Murphy et al., 2024). These similarities in the conserved areas further pique researchers’ interest in developing a broad-spectrum antimalarial drug (Murphy et al., 2024).
AlphaFold has identified functional domains or regions in the PF3D7_1430500 protein structure, namely P34-Arc. This domain forms part of the Arp2/3 complex involved in the actin filament nucleation process, essential for parasite cell division and gametocyte maturation (Behrens & Spielmann, 2024).
Furthermore, compared to human proteins, molecular mimicry of P. falciparum proteins was discovered – 41 possible mimicry interactions; this revealed the mechanism by which these parasites avoid immune detection (Muthye & Wasmuth, 2023).
In addition, antimalarial drug resistance proves to be an ongoing challenge. Changes in protein structures have identified Kelch 13 (Pfk13) mutations of F446I and C580Y that affect hydrogen atom bonding and other binding qualities (Yan et al., 2022). This aids in understanding the mechanisms that contribute to, for example, artemisinin resistance (Abugri et al., 2022; Yan et al., 2022).
AlphaFold's key protein structures include Pfs48/45, integral to parasite development (Ko et al., 2022; Yan et al., 2022). Previous attempts to investigate this protein proved challenging due to the low-resolution images produced with cryo-EM and X-ray crystallography. Other organelles and proteins modeled include proteases and casein kinase 2 (PfCK2), which are involved in parasite survival and allow it to reproduce (Yan et al., 2022).
Predicting Structures of Plasmodium spp and beyond
An amino acid transporter, PvApiAT8, in Plasmodium vivax was predicted to highlight possible target sites for drug development. It specifically focused on its 12 transmembrane domains, disrupting nutrient transport for parasite survival.
Additionally, the structure prediction of PfCLK3 has been studied for inhibitor development, as this protein is present in different stages of the parasite life cycle.
AlphaFold’s application has been extended to other parasitic conditions, such as Chagas Disease (Trypanosoma cruzi), Sleeping Sickness (Trypanosoma brucei), and Leishmaniasis. The focus is on identifying molecular targets, optimizing current treatments, and elucidating drug resistance and parasite survival cases through protein structure prediction.
Targeted Drug and Vaccine Development
AlphaFold’s predictive structure application can be integrated with bioinformatics, revealing protein interactions capable of drug targeting (Abugri et al., 2022). These targets include proteases and metabolic pathways of the parasite’s life cycle. In addition, further investigation exhibited binding sites on Pfs48/45, indicating where transmission-blocking antibodies can bind (Ko et al., 2022). These two approaches highlight the development and design of therapeutic drugs and vaccine design directed against specific targets of this blood parasite. Recently, ongoing vaccine development has been focused on targeting Pfs48/45, a core gamete surface protein, which is essential to prevent parasite transmission by rendering the development of the parasite in mosquitoes (Ko et al., 2022). AlphaFold structural insights have also enabled addressing multiple stages of vaccine development, targeted against specific proteins present throughout different phases of the parasite’s life cycle (Abugri et al., 2022).
Cancer Development – Misfolded Proteins Driving Oncogenic Progress
The process of protein misfolding emphasizes that upon protein synthesis, it does not correctly conform to its three-dimensional structure. Various factors influence the production of misfolded proteins, such as environmental stressors, aging, and genetic mutations. This, in turn, leads to protein aggregation forming toxic complexes, adverse effects on cellular processes, altered cellular signaling, and bypassing quality control mechanisms during the cell cycle (mitosis).
Applying AlphaFold provides insights into mutational structures, highlights protein regions permissible for aggregation, and identifies sections within proteins where their normal functioning is affected (Yang et al., 2023).
This is where AlphaFold’s predictive modeling system has already made leaps to address and elucidate oncogenic behavior. Predicting accurate protein misfolding, which is associated with cancer development, instills knowledge about the mechanisms that drive dysregulated cellular proliferation associated with cancers (Yang et al., 2023).
Protein Mutations
It also clarifies the mutations affecting specific genes (proto-oncogenes) that encode proteins, leading to protein dysregulation, such as structural alteration and functional changes (Chandrasekera, 2021). One example includes predicting mutations of the P53 gene, which is generally characterized by suppressing tumor development. However, when dysregulated, it leads to an inability to repair DNA and apoptosis of oncogenic cells, allowing tumor cells to thrive. Another instance is where the KRAS oncogene prediction highlighted altered protein activity and loss of DNA repair functionality. This is like a mutational examination of BRCA1 and how mutations such as A1708E change its interaction with other proteins responsible for genome stability maintenance (Zheng et al., 2024). Additional evidence includes heat shock proteins (HSPs) capable of protein folding management and mitigating aggregates of dysregulated proteins. Cancerous cells affect HSPs, resulting in mutated proteins such as RAS and HER2 to stabilize in their cellular environment. AlphaFold also proves to identify sections of protein misfolding, causing disruptive cellular homeostasis and converging to drive oncogenesis.
Predicting Pathogenicity
AlphaFold’s confidence scores, or predicted local distance difference test (pLDDT), have proven to outperform traditional methods for stability prediction relating to pathogenic variation determination; a study by Cheng et al. (2023) indicated using an AlphaFold derivative, AlphaMissense, for highly accurate pathogenicity prediction. Determining the potential of misfolded protein pathogenicity using a simplified model to correlate genetic mutations with cancer-associated risks, while evaluating the reliability of structure predictions, emphasizes identifying protein stability prone to disruption or altered interactions. Research by Karakoyun et al. (2023) indicated that structure prediction for missense variations indicated an AUROC (area under the receiver operating characteristic) of 0.852; an AUROC value of 0.8 to 0.9 is considered excellent, indicative of accurately identifying the pathogenic presence in protein structures, and whether it plays an integral role in cancer risk and progression (Qui et al., 2024).
Protein-protein interactions
AlphaFold models dysregulation of protein-protein interactions involved in cancer development and elucidates possible mutations related to abnormal protein interactions. This interactive process is essential for regulating cell cycles, mediating apoptosis, and immune evasion. When models of dysregulated protein interactions are generated, they can suggest how and why the cell proliferation process becomes uncontrolled and apoptosis is resisted. What further elevates this approach is the integration of AlphaFold with generated spatial models consisting of protein complexes, where new or previously unknown protein interactions can be identified, enhancing new approaches to therapeutic mediation (Raisinghani et al., 2024). For example, ABL kinase shape change predictions enable understanding allosteric regulation and drug target identification (Raisinghani et al., 2024).
Intrinsically Disordered Regions (IDRs) Linked to Cancer Development
IDRs were extensively discussed in a previous article; however, we briefly mention their role in cancer development in this section.
Many cancer-related proteins contain IDRs, such as MYC Transcription factors or signaling molecules. Conventional methods have experimentally struggled to predict these dynamic structures, but AlphaFold enables the investigation of their predicted structures and interactions and how they contribute to cancer development (Qui et al., 2024).
Human Proteome Prediction
AlphaFold’s invaluable contribution to human proteome prediction enables researchers to investigate protein structures for possible disordered regions, especially proteins implicated in specific cancers. Porta-Pardo et al. (2022) revealed that by integrating AlphaFold and the Protein Databank’s (PDBs) previously acquired predictive knowledge, an overall 70% coverage of high-quality cancer-associated genes was predicted. AlphaFold’s input is advantageous because it accelerates the process of guiding tumor biology understanding through patient-specific protein variations specific to each patient’s cancer heterogeneity, which relates to the uniqueness of each oncogenic mutation. This, in turn, further suggests the identification of dysregulated proteins that can fast-track drug development and targeted therapeutic approaches (Porta-Pardo et al., 2022).
Drug Target Discovery and Development
Specifically in drug target investigation for cancer development, the discovery of targets associated with certain cancers enables the development of therapeutic drugs, such as inhibitor designs that target proteins like CDK20 (Cyclin-Dependent Kinase 20), which is known to cause uncontrolled cell proliferation in hepatocellular carcinoma (Ren et al., 2023; Yang et al., 2023).
Targeted Biomarker Identification
Extending AlphaFold’s methodologies to biomarker discovery through structure prediction of specific proteins associated with a particular cancer or cancer pathway provides an improved approach to early intervention and cancer progression surveillance (Yang et al., 2023). One example is the structural variety of proteins linked with breast cancer, such as BRCA1, and identifying patient-specific indicators related to colorectal cancer (Zheng et al., 2024).
Applying AlphaFold’s Structure Prediction to Viral and Bacterial Studies
Understanding Viruses
Viruses are microscopic parasitic agents that infect a host's cell, causing disease. Hosts can be humans, animals, fungi, plants, and bacteria. Different viral species can cause various human illnesses, such as respiratory diseases, diarrhea, vomiting, sexually transmitted infections, and skin conditions. Viruses interact with and comprise protein structures, making them highly relevant to AlphaFold's prediction models.
Viruses contain various components, such as genetic material – RNA and DNA, enclosed by a capsid membrane layer of protein structures. Some viral species have an additional layer that envelops the capsid membrane, while others, colloquially referred to as "naked viruses," do not (The Cleveland Clinic, n.d.). Viruses require a host cell that can read their RNA instructions to produce protein and replicate themselves (The Cleveland Clinic, n.d.).
Viral Invasion and Replication
Viruses can enter host cells through receptor binding, direct fusion, and bacteriophage injection. In receptor binding, viruses enter cells by binding to protein receptors on the cell's membrane surface; these receptors act as "doors" that fool the cell into letting the virus enter (The Cleveland Clinic, n.d.). In direct fusion, some viruses attach directly to the cell membrane itself, enabling them to diffuse their genetic contents into the cytoplasm of the infected host (The Cleveland Clinic, n.d.). Others inject their genetic material into bacterial cells without having to enter them. This process is known as bacteriophage injection (The Cleveland Clinic, n.d.).
Once a virus or its genetic material has entered a host, it reproduces in one of two ways: the lytic and lysogenic cycles. Some utilize both methods. In the lytic cycle, the virus infects a host, hijacks its genetic reproductive system, and replicates copies of itself until the host bursts in lysis (Roughgarden, 2024). The virions then go on to infect other host cells. In the lysogenic cycle, the virus integrates directly into the host's genome without the host's awareness (Roughgarden, 2024). The host then reproduces, usually passing on its tampered genetic makeup to its offspring, further proliferating infected cells (Roughgarden, 2024).
AlphaFold’s Application in Viral Research
AlphaFold has many profound implications in viral research. This includes drug therapy, vaccine development, and even the development of bacteriophages that target specific bacterial pathogens. This paves the way for many significant medical and public health contributions.
Coronavirus, or SARS-CoV-2, is an example of a virus that AlphaFold can analyze. SARS-CoV-2 has a receptor-binding protein (RBP) that interacts with another protein, the angiotensin-converting enzyme 2 (ACE2) receptor. The ACE2 receptor is found on the surfaces of various cells throughout the body and serves as a passageway for the entrance of the virus, making its study critical to understanding COVID-19 disease mechanisms (Ni et al., 2020). AlphaFold 2 predictions uncovered that monomeric, rather than multimeric, formulations of the COVID-19 vaccines had more antigenic epitopes; in other words, it was found that vaccines with single molecules on their surface (monomers) elicited an immune response and bound better to antibodies than multimeric, or multiple molecular, vaccines (Gutnik et al., 2023). Furthermore, the discovery of new variants of the virus, such as Omicron BA1, made it critical to predict mutation sites not yet discovered, which could increase the binding affinity of the receptor-binding domain (RBD) or, in simpler terms, bind the virus more strongly to human cells (Gutnik et al., 2023).
AlphaFold's application is not limited to the coronavirus; other eukaryotic viruses, such as monkeypox (MPXV), herpes simplex virus, and hepatitis E virus, can also be researched using AlphaFold's protein prediction mechanism. Taking MPXV as an example in this case, AlphaFold predicted a structure that could bind to MPXV's DNA Polymerase (DNAP), an antiviral binding target, comparable in efficiency to that of existing anti-smallpox drugs, such as brincidofovir and cidofovir (Gutnik et al., 2023). Additionally, viruses can mutate and develop resistance to drugs. In the context of MPVX, drug resistance can arise due to mutations in proteins of the DNA replication complex (RC). AlphaFold's technology could predict mutations in the MPVX RC that conferred similar levels of drug resistance to that of cidofovir in monkeypox and vaccinia viruses (Gutnik et al., 2023).
Understanding Bacteria: Morphology and Mode of Action
On the other hand, bacteria are single-celled, microscopic prokaryotic organisms ubiquitous across the planet. While some are beneficial, others, known as pathogens, can harm human health. Fortunately, most species are relatively harmless, with only a few causing disease. The human body has an abundance of bacteria; in fact, there are estimated to be 38 trillion bacteria in the body, which far outnumber human cells (Sender, 2016). Bacteria are vital in maintaining gut health, primarily regarding digestion, metabolism, and immunity (The Cleveland Clinic, n.d.).
Anatomically, bacteria, being prokaryotes, lack nuclei and membrane-bound organelles. They come in various shapes, such as spheres, cylinders, rods, spiral heads, and chains (FSU, 2015). Bacteria typically have three layers of membrane: capsule, cell wall, and cytoplasmic membrane. In addition, they possess nucleoids in lieu of nuclei, which contain their genetic material. Many bacterial species also possess pili, which are hairlike growths on the outer surface that aid them in attaching to surfaces and conjugation, the genetic exchange of plasmid DNA between bacteria during reproduction (FSU, 2015). These pili structures are also instrumental in enabling pathogenic bacteria to infect others because they help them attach to host surfaces (FSU, 2015). Like eukaryotic cells, bacteria possess ribosomes, small organelles distributed throughout their cytoplasm that read genetic strands to produce proteins. Antibiotics effectively target these ribosomes to kill pathogenic bacteria (FSU, 2015). Pathogenic bacteria include a number of different species that harm human health. Bacteria can be classified as either intracellular or extracellular. Intracellular bacteria primarily grow and thrive inside a host's cells, whereas extracellular bacteria grow and thrive outside cells by evading the host's immune response system (Soni et al., 2024). Mycobacterium tuberculosis, Listeria monocytogenes, and Chlamydia trachomatis are a few examples of pathogenic intracellular bacteria capable of causing disease, such as tuberculosis; meanwhile, extracellular pathogenic bacteria include Staphylococcus aureus, Streptococcus pyogenes, and Escherichia coli, which cause wound infections, scarlet fever, urinary tract infection, etc. (Soni et al., 2024).
Using AlphaFold for Bacterial Research Applications
We can see how bacteria also have important implications in medical research that AlphaFold can capitalize on. These varying applications, such as bacteriophage development, improving a healthy gut biome, and developing effective antibiotics against bacterial resistance, can confer advancements in microbiological aspects of medicine and public health. Exploring some examples of ways AlphaFold optimizes our understanding of bacteria and their role in preserving health is helpful.
Bacteriophages are viral agents that infect bacterial hosts and destroy them. They are essential in developing antimicrobial drugs to target specific pathogens through a process known as phage therapy (Gutnik et al., 2023). AlphaFold's algorithm can shed light on bacterial host receptors and phage receptor binding proteins to help us understand these mechanisms better than traditional methods by enabling us to see how and where a bacteriophage binds to its intended target (Gutnik et al., 2023). This can deepen our knowledge of these interactions and how potential microbial mutations confer resistance to bacteriophages.
There is a growing need to address antibiotic resistance in the context of bacteria. Antibiotic resistance occurs when strains of bacteria mutate and develop immunity against certain antibiotics, rendering them futile. Antibiotic resistance is controlled by enzymes, which can be blocked to enable antibiotics to continue functioning (DeepMind, 2022). AlphaFold can revolutionize this approach by predicting the molecular function and structure of antibiotic resistance compounds and strengthening the pathogenic targeting of antibiotics (Behling et al., 2023). Indeed, research using AlphaFold has already demonstrated a few breakthrough discoveries, such as developing antimicrobial peptides using in vivo protein synthesis and predicting novel protein structures associated with antibiotic resistance (Behling et al., 2023).
Furthermore, AlphaFold can help us understand and promote better gut health. Gut microbiome imbalance is linked to multiple immune disorders, such as autoimmune, allergic, and chronic inflammatory disorders (Taneishi & Tsuchiya, 2022). This all involves an interplay of protein structure interactions. The gut bacteria are also responsible for influencing glycosylation, which is the addition of a carbohydrate, or sugar molecule, to a protein; this process changes how proteins interact and move inside the body (Technology Networks, 2025). Recently, a study showcased how glycosylation in the gut has been implicated in protein brain modifications, such as enhancing cognition and promoting neuron axon growth (Technology Networks, 2025). However, glycosylation has been an extremely complex process to study, incurring time and resources (Technology Networks, 2025). With AlphaFold, it can facilitate the study of glycosylated proteins via its prediction algorithms, which can aid in reproducing glycosylated proteins without having to go through the laborious process of concentrating enough of them from the body for further study and analysis.
How AlphaFold Addresses Public Health Risks Worldwide
In recent years, we have seen the rise of both pandemics and increasing antibiotic-resistant infections. In fact, despite modern advancements in antibiotics, there are an estimated staggering 2.8 million antibiotic-resistant infections in the United States alone (DeepMind, 2022). Contrary to popular assumption, the COVID-19 vaccine was not hurriedly developed within a year but was in development for well over a decade prior to the outbreak due to research on smaller-scale viral outbreaks in the Middle East and East Asia (Berg, 2021). This highlights an urgent need to tackle biological threats with speed and efficiency.
So, what do these findings mean in the grand scheme of things? Countering infectious viruses and pathogenic bacteria is a cornerstone of public health. AlphaFold can optimize our understanding of various protein-binding mechanisms and help us predict mutations before they arise so that we can develop targeted strategies to overcome them. With this in mind, we can accelerate research into viral outbreaks and antibiotic-resistant infections before they pose significant threats to human life. Instead of waiting a decade to develop a vaccine for an unforeseen pandemic, we could shorten that time significantly by utilizing AlphaFold's strategic predictive prowess.
Accelerating Drug Discovery and Providing Personalized Therapeutic Approaches
AlphaFold's impact extends significantly into drug discovery and personalized medicine. The ability to rapidly and accurately predict not only protein structures but also the structures of complexes involving other biomolecules has profound implications for accelerating the identification and development of new therapeutics and tailoring treatments to individual patients.
Accelerated Drug Discovery with AlphaFold
The traditional drug discovery process is notoriously lengthy and expensive. It often takes over a decade and billions of dollars to bring a new drug to market. AlphaFold has the potential to dramatically streamline this process by providing rapid, accurate structural information at multiple stages of the pipeline (Borkakoti & Thornton, 2023).
Target Identification and Validation: Identifying proteins involved in disease processes (referred to as drug targets) is a crucial first step. AlphaFold, particularly with the advancements of AlphaFold 3, helps researchers identify and validate potential drug targets by providing structural information about proteins, protein-protein interactions, and even interactions with DNA and RNA that were previously difficult or impossible to characterize experimentally (Abramson et al., 2024).
Drug Design with AlphaProteo: The introduction of AlphaProteo, characterized as an extension of AlphaFold’s abilities, allows for de novo protein design by utilizing the protein prediction models to identify and create molecules capable of binding and adhering to specific active or allosteric sites on proteins (Zambaldi et al., 2024). AlphaProteo’s ability reaches as far as designing synthetic molecules that target specific proteins associated with certain conditions, which also characteristically bind more effectively than conventional methods to their targeted proteins. It also uses thermodynamic properties and kinetic binding calculations to elucidate and support drug-target interactions related to binding. Integrating AlphaProteo with molecular dynamic simulations of drug-protein interactions enhances the determination of the stability of these complexes (Zambaldi et al., 2024). A prime example is AlphaProteo’s contribution to accelerating understanding of the SARS-CoV-2 spike protein receptor-binding domain (SC2RBD), which is vital in rapid vaccine development by disrupting the human ACE2 receptor interaction (AlphaFold: The AI Breakthrough, 2024; Demirkaya, 2024; Zambaldi et al., 2024). Other instances include targeting the binding site of interleukin-7 Receptor-a (IL-7RA), a therapeutic target of lymphocyte development in acute lymphoblastic leukemia and HIV, as well as Tumor Necrosis Factor-a (TNF-a), a proinflammatory cytokine and therapeutic target for inflammation-associated conditions (Zambaldi et al., 2024).
Structure-Based Drug Design (SBDD) and Pharmaceutical Collaboration: SBDD relies on understanding the 3D structure of a target protein (and its interactions) to design molecules that can bind to it and modulate its activity (Borkakoti & Thornton, 2023). AlphaFold 3 provides high-quality structural models, including protein-ligand complexes, with significantly improved accuracy compared to earlier versions (Abramson et al., 2024; Review of AlphaFold 3, 2024). This in silico approach, using virtual screening and lead optimization, significantly reduces the reliance on expensive and time-consuming traditional methods (Demirkaya, 2024). Isomorphic Labs, a company spun off from DeepMind, is actively collaborating with pharmaceutical companies, demonstrating the practical application of AlphaFold in this area (Desai et al., 2024; Review of AlphaFold 3, 2024). For example, pharmaceutical collaboration with Eli Lilly focuses on small-molecule therapeutics such as kinase inhibitors and antibody-drug conjugates, and Novartis applies AlphaFold’s structural insights toward gene therapy (CRISPR-Cas system) and designing nucleotide analogs targeting viral infections (Gandharv & Dey, 2024).
Understanding Drug Resistance: AlphaFold can help predict how protein mutations might lead to drug resistance. By comparing the structures of wild-type and mutant proteins, including their interactions with potential drugs, researchers can gain insights into how drug-binding sites change, informing the design of drugs that are less susceptible to resistance (Integrating AlphaFold Into the Drug Discovery Process, n.d.). For instance, applying protein prediction algorithms enabled researchers to identify bacteriophage lysins capable of penetrating bacterial cell walls, highlighting an alternative approach to antibacterial drug design (Gutnik et al., 2023; Desai et al., 2024).
Repurposing Existing Drugs: AlphaFold can identify potential new uses for existing drugs. By predicting the structures of proteins and their interactions with known drugs, researchers can identify novel targets and diseases these drugs might be effective against, significantly shortening development timelines (Desai et al., 2024).
Antibody Design: AlphaFold has proven to be highly effective at predicting the structures of antibodies, which are crucial components of many therapies. This capability accelerates new antibody-based drug development (Lupyan & Engelhardt, 2022).
Personalized Medicine Advancement
Personalized medicine aims to tailor medical treatment to each patient's characteristics, considering genetic variations, lifestyle, and environmental factors. AlphaFold provides a powerful tool for understanding the structural consequences of genetic variations, enabling a more personalized approach to healthcare.
Understanding Genetic Variations: AlphaFold can predict the structural consequences of genetic variations in individuals. This is crucial for understanding how these variations might contribute to disease susceptibility or drug response. By modeling the structures of protein variants, researchers can gain insights into personalized treatment strategies (Jumper et al., 2021; Desai et al., 2024).
Tailoring Treatments: The ability to predict the structures of patient-specific protein variants and their interactions with potential drugs extends the possibility of designing personalized therapies. This is particularly relevant in cancer, where tumors often harbor unique mutations affecting drug response. AlphaFold, particularly with its ability to model protein complexes and modifications, can aid in designing treatments tailored to the specific molecular profile of an individual's disease (Abramson et al., 2024).
Predicting Protein Interactions: AlphaFold can go beyond individual protein structures, predicting the interactions of various molecules important in the body. This is critical in understanding diseases on a holistic level, which helps develop personalized medicines (Akdel et al., 2022; Desai et al., 2024).
Ethical Considerations for Personalized Therapeutic Approaches
As AI plays a growing role in healthcare, addressing ethical issues is very important. The increasing power of AI in biomedicine, specifically in personalized medicine, raises important ethical considerations. Protecting patient privacy and ensuring the responsible use of AI-driven insights are crucial aspects that must be addressed alongside scientific advancements (AI-based AlphaFold: Its potential impact, 2024).
Current Limitations of AlphaFold and Future Research
It is important to note that while AlphaFold represents a monumental leap forward, it is not without limitations. As discussed in [Article 1], accurately modeling proteins' dynamic motions and conformational changes remains challenging, and like other AI models, AlphaFold can sometimes generate structures that are not physically realistic (referred to as hallucinations) (Desai et al., 2024); however, active research continues to address these shortcomings (AlphaFold 3 — What's Next, 2024). Furthermore, AlphaFold complements, but does not replace, experimental methods like X-ray crystallography and cryo-electron microscopy (cryo-EM), and the need for computational frameworks capable of integrating large-scale datasets from various omics modalities is necessary (Krokidis et al., 2024). In addition, integrating molecular simulations of dynamic protein folding processes is required for future protein shape change investigation (Krokidis et al., 2024). Finally, the commercial use of the recently released AlphaFold 3 code remains restricted, even though it can be accessed for non-commercial use through the AlphaFold Server (AlphaFold 3: New Possibilities, 2024; Desai et al., 2024), representing a practical limit to its adoption.
Considering all of this, AlphaFold 3's advancements are revolutionizing drug discovery and paving the way for personalized medicine. Providing rapid, accurate, and increasingly comprehensive structural predictions accelerates the identification of new drug targets, facilitates the design of more effective therapies, and enables a more individualized approach to patient care. The continued development and application of AlphaFold, along with complementary technologies, hold immense promise for transforming the future of medicine.
Prospective Integrated Multimodal collaboration with AlphaFold Technology
The integrative power of AlphaFold combined with multimodal approaches, including multi-omics data, provides a comprehensive view of protein structure modeling, dynamic changes, and the environmental interaction that occurs under certain conditions (Li et al., 2021).
Using protein prediction as a foundation for insights into the structural architecture of protein folding and combining it with additional omics data, such as genomics, transcriptomics, proteomics, and metabolomics, enhances approaches to navigating the intricacies of biological mechanisms associated with specific disorders. For example, integrating AlphaFold 3 with proteomic quantitative trait loci (pQTLs) data or genome-wide associated studies (GWAS) enables the identification of druggable protein targets by examining sequence lengths and per-residue confidence scores (pLDDT) (Desai et al., 2024), ultimately contributing to drug development.
Furthermore, the gap between genomics and structural function can be addressed by studying genetic variations that impact protein expression, which aids in identifying protein stability and function specific to certain tissues, such as the brain or cerebrospinal fluid. It identifies how misfolding and areas susceptible to protein aggregation lead to neurodegenerative pathogenesis, as seen in genetic alterations encoding amyloid precursor protein (APP) or tau proteins. Similarly, structure predictions can elucidate functional alterations associated with single-nucleotide polymorphisms (SNPs) seen with protein-protein interactions or enzyme activity. An example includes the integrative power of AlphaFold with molecular dynamics (e.g., MoDAFold platform) for missense mutant structures by harnessing the strength of both methodologies (Zheng et al., 2024).
Moreover, building a vast network-based framework using AlphaFold’s three-dimensional models, combined with genomic, transcriptomic, and proteomic datasets, contributes to understanding a protein-protein interactive system (Desai et al., 2024; Nourbakhsh et al., 2024). The impact of such a network highlights how common pathways are implicated in neurodegenerative conditions through autophagy, immune regulation, and metabolic processes, such as oxidative stress and an inflammatory state. This is also where more complex machine-learning models can be combined with imaging methodologies, for instance, functional Magnetic Resonance Imaging (fMRI) or electroencephalogram (EEG), alongside genetic patterns for disease classification and the discovery of potential biomarkers (Nourbakhsh et al., 2024).
The Impact of Future Multimodal Integration with AlphaFold
Integrating datasets of genetic variations with protein structure and function predictions provides insight into drug discovery and development specific to an individual’s genetic makeup and associated conditions (Desai et al., 2024).
Combining different modalities enables understanding the pathogenesis of specific disorders and their effect on signaling pathways essential for neuronal function. It also reveals shared pathways across different diseases, enabling a comprehensive understanding of the various components involved in the associated condition. Examples include synaptic dysfunction in AD or neuroinflammation associated with PD.
Mapping disease pathways, such as elucidating proto-oncogenes and oncogenes as the driving force behind cancer progression, enables the development of diagnostic tests highlighting specific pathways in cancer development for early screening and prevention tactics (Chandrasekera, 2021).
Conclusion
At this stage, the impact of AlphaFold’s structural insights on protein prediction is well-known; however, its implications for understanding various disease mechanisms are only the beginning, as indicated by ongoing real-world research. With continuous advancements in AlphaFold’s technology, such as multimodal integration and additional omics data, the future of disease causes will be upended and revealed. The next step? Continued druggable target discovery, furthering therapeutic drug repurposing or design, and integrating genetic information with an individual’s specific condition for personalized medical care.
As AlphaFold and the integrative power of additional computational biology tools continue to expand, we are nearing an understanding of the intricacies of previously unresolved debilitating conditions; this emphasizes a future of healthcare improvement at its tipping point.
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Lead Researcher and Writer: Renaldo Pool, BHSc Research and Writing: David Matin, B.A. and Cindy Hamilton, MPH Review & Editing: Larrie Hamilton, MHC Visualization and Final Edit: Michelle Ryan, MHA Conceptualization and Methodology: David Priede, PhD Project Administration and Funding Acquisition: BioLife Health Research Center |
