Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction

Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER

The dominant success of deep learning techniques on protein structure prediction has challenged the necessity and usefulness of traditional force field-based folding simulations. We proposed a hybrid approach, deep-learning-based iterative threading assembly refinement (D-I-TASSER), which constructs atomic-level protein structural models by integrating multisource deep learning potentials with iterative threading fragment assembly simulations. D-I-TASSER introduces a domain splitting and assembly protocol for the automated modeling of large multidomain protein structures. Benchmark tests and the most recent critical assessment of protein structure prediction, 15 experiments demonstrate that D-I-TASSER outperforms AlphaFold2 and AlphaFold3 on both single-domain and multidomain proteins. Large-scale folding experiments further show that D-I-TASSER could fold 81% of protein domains and 73% of full-chain sequences in the human proteome with results highly complementary to recently released models by AlphaFold2. These results highlight a new avenue to integrate deep learning with classical physics-based folding simulations for high-accuracy protein structure and function predictions that are usable in genome-wide applications.

Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification

The microbiome of the gut is a complex ecosystem that contains a wide variety of microbial species and functional capabilities. The microbiome has a significant impact on health and disease by affecting endocrinology, physiology, and neurology. It can change the progression of certain diseases and enhance treatment responses and tolerance. The gut microbiota plays a pivotal role in human health, influencing a wide range of physiological processes. Recent advances in computational tools and artificial intelligence (AI) have revolutionized the study of gut microbiota, enabling the identification of biomarkers that are critical for diagnosing and treating various diseases. This review hunts through the cutting-edge computational methodologies that integrate multi-omics data-such as metagenomics, metaproteomics, and metabolomics-providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. By highlighting the synergy between traditional bioinformatics tools and advanced AI techniques, this review underscores the potential of these approaches in enhancing biomarker discovery and developing personalized therapeutic strategies. The convergence of computational advancements and microbiome research marks a significant step forward in precision medicine, paving the way for novel diagnostics and treatments tailored to individual microbiome profiles. Investigators have the ability to discover connections between the composition of microorganisms, the expression of genes, and the profiles of metabolites. Individual reactions to medicines that target gut microbes can be predicted by models driven by artificial intelligence. It is possible to obtain personalized and precision medicine by first gaining an understanding of the impact that the gut microbiota has on the development of disease. The application of machine learning allows for the customization of treatments to the specific microbial environment of an individual.

Improving AlphaFold2 and 3-based protein complex structure prediction with MULTICOM4 in CASP16

With AlphaFold achieving high-accuracy tertiary structure prediction for most single-chain proteins (monomers), the next major challenge in protein structure prediction is accurately modeling multi-chain protein complexes (multimers). We developed MULTICOM4, the latest version of the MULTICOM system, to improve protein complex structure prediction by integrating transformer-based AlphaFold2, diffusion model-based AlphaFold3, and our in-house techniques. These include protein complex stoichiometry prediction, diverse multiple sequence alignment (MSA) generation leveraging both sequence and structure comparison, modeling exception handling, and deep learning-based model quality assessment. MULTICOM4 was blindly evaluated in the 16th community-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP16) in 2024. In Phase 0 of CASP16, where stoichiometry information was unavailable, MULTICOM predictors performed best, with MULTICOM_human achieving a TM-score of 0.752 and a DockQ score of 0.584 for top-ranked predictions on average. In Phase 1 of CASP16, with stoichiometry information provided, MULTICOM_human remained among the top predictors, attaining a TM-score of 0.797 and a DockQ score of 0.558 on average. The CASP16 results demonstrate that integrating complementary AlphaFold2 and 3 with enhanced MSA inputs, comprehensive model ranking, exception handling, and accurate stoichiometry prediction can effectively improve protein complex structure prediction.

Leveraging Sequence Purification for Accurate Prediction of Multiple Conformational States with AlphaFold2

AlphaFold2 (AF2) has transformed protein structure prediction by harnessing co-evolutionary constraints embedded in multiple sequence alignments (MSAs). MSAs not only encode static structural information, but also hold critical details about protein dynamics, which underpin biological functions. However, these subtle coevolutionary signatures, which dictate conformational state preferences, are often obscured by noise within MSA data and thus remain challenging to decipher. Here, we introduce AF-ClaSeq, a systematic framework that isolates these co-evolutionary signals through sequence purification and iterative enrichment. By extracting sequence subsets that preferentially encode distinct structural states, AF-ClaSeq enables high-confidence predictions of alternative conformations. Our findings reveal that the successful sampling of alternative states depends not on MSA depth but on sequence purity. Intriguingly, purified sequences encoding specific structural states are distributed across phylogenetic clades and superfamilies, rather than confined to specific lineages. Expanding upon AF2's transformative capabilities, AF-ClaSeq provides a powerful approach for uncovering hidden structural plasticity, advancing allosteric protein and drug design, and facilitating dynamics-based protein function annotation.

Advancing protein structure prediction beyond AlphaFold2

Accurate prediction of protein structures is essential for understanding their biological functions. The release of AlphaFold2 in 2021 marked a significant breakthrough, delivering unprecedented accuracy. However, challenges remain, particularly for proteins with limited evolutionary data or complex molecular interactions. This review explores efforts to enhance AlphaFold2's performance through advanced sequence search techniques and alternative approaches, including protein language models and frameworks that integrate diverse biomolecular interactions. We propose that future progress will depend on developing models grounded in fundamental physicochemical principles, offering more accurate and comprehensive predictions across a wider spectrum of biological systems.

Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction

Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularly with the development of AlphaFold2 (AF2), which can analyze sequences of amino acids and predict 3D structures with near experimental accuracy. Since the release of AF2, numerous studies have been conducted, either using AF2 directly for large-scale modeling or building upon the software for other use cases. Many reviews have been published discussing the impact of AF2 in the field of protein bioinformatics, particularly in relation to neural networks, which have highlighted what AF2 can and cannot do. It is evident that AF2 and similar approaches are open to further development and several new approaches have emerged, in addition to older refinement approaches, for improving the quality of predictions. Here we provide a brief overview, aimed at the general biologist, of how machine learning techniques have been used for improvement of 3D models of proteins following AF2, and we highlight the impacts of these approaches. In the most recent experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP15), the most successful groups all developed their own tools for protein structure modeling that were based at least in some part on AF2. This improvement involved employing techniques such as generative modeling, changing parameters such as dropout to generate more AF2 structures, and data-driven approaches including using alternative templates and MSAs.

The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction

Multiple sequence alignment (MSA) has evolved into a fundamental tool in the biological sciences, playing a pivotal role in predicting molecular structures and functions. With broad applications in protein and nucleic acid modeling, MSAs continue to underpin advancements across a range of disciplines. MSAs are not only foundational for traditional sequence comparison techniques but also increasingly important in the context of artificial intelligence (AI)-driven advancements. Recent breakthroughs in AI, particularly in protein and nucleic acid structure prediction, rely heavily on the accuracy and efficiency of MSAs to enhance remote homology detection and guide spatial restraints. This review traces the historical evolution of MSA, highlighting its significance in molecular structure and function prediction. We cover the methodologies used for protein monomers, protein complexes, and RNA, while also exploring emerging AI-based alternatives, such as protein language models, as complementary or replacement approaches to traditional MSAs in application tasks. By discussing the strengths, limitations, and applications of these methods, this review aims to provide researchers with valuable insights into MSA's evolving role, equipping them to make informed decisions in structural prediction research.

Petabase-Scale Homology Search for Structure Prediction

The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, we conducted a petabase-scale search of the Sequence Read Archive (SRA), resulting in gigabytes of aligned homologs for CASP15 targets. These were merged with default MSAs produced by ColabFold-search and provided to ColabFold-predict. By using SRA data, we achieved highly accurate predictions (GDT_TS > 70) for 66% of the non-easy targets, whereas using ColabFold-search default MSAs scored highly in only 52%. Next, we tested the effect of deep homology search and ColabFold's advanced features, such as more recycles, on prediction accuracy. While SRA homologs were most significant for improving ColabFold's CASP15 ranking from 11th to 3rd place, other strategies contributed too. We analyze these in the context of existing strategies to improve prediction.

Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data

Leveraging iterative alignment search through genomic and metagenome sequence databases, we report the DeepMSA2 pipeline for uniform protein single- and multichain multiple-sequence alignment (MSA) construction. Large-scale benchmarks show that DeepMSA2 MSAs can remarkably increase the accuracy of protein tertiary and quaternary structure predictions compared with current state-of-the-art methods. An integrated pipeline with DeepMSA2 participated in the most recent CASP15 experiment and created complex structural models with considerably higher quality than the AlphaFold2-Multimer server (v.2.2.0). Detailed data analyses show that the major advantage of DeepMSA2 lies in its balanced alignment search and effective model selection, and in the power of integrating huge metagenomics databases. These results demonstrate a new avenue to improve deep learning protein structure prediction through advanced MSA construction and provide additional evidence that optimization of input information to deep learning-based structure prediction methods must be considered with as much care as the design of the predictor itself.

Structure-guided metagenome mining to tap microbial functional diversity

Scientists now have access to millions of accurate three-dimensional (3D) models of protein structures. How do we leverage 3D structural models to learn about microbial functions encoded in metagenomes? Here, we review recent developments using protein structural features to mine metagenomes from diverse environments ranging from the human gut to soil and ocean viromes. We compare 3D protein structural methods to characterize antibiotic resistance phenotypes, nutrient cycling, and host-drug-microbe interactions. Broadly, we encourage the scientific community to look beyond global sequence and structure alignments by considering fine-grained descriptors such as distance to ligand, active site, and tertiary interactions between amino acid residues scaling to microbiomes. Finally, we highlight structure-inspired approaches to chart new areas of microbial protein-coding sequence space.

Integrating deep learning, threading alignments, and a multi-MSA strategy for high-quality protein monomer and complex structure prediction in CASP15

We report the results of the "UM-TBM" and "Zheng" groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were obtained using the D-I-TASSER and DMFold-Multimer algorithms, respectively. For monomer structure prediction, D-I-TASSER introduced four new features during CASP15: (i) a multiple sequence alignment (MSA) generation protocol that combines multi-source MSA searching and a structural modeling-based MSA ranker; (ii) attention-network based spatial restraints; (iii) a multi-domain module containing domain partition and arrangement for domain-level templates and spatial restraints; (iv) an optimized I-TASSER-based folding simulation system for full-length model creation guided by a combination of deep learning restraints, threading alignments, and knowledge-based potentials. For 47 free modeling targets in CASP15, the final models predicted by D-I-TASSER showed average TM-score 19% higher than the standard AlphaFold2 program. We thus showed that traditional Monte Carlo-based folding simulations, when appropriately coupled with deep learning algorithms, can generate models with improved accuracy over end-to-end deep learning methods alone. For protein complex structure prediction, DMFold-Multimer generated models by integrating a new MSA generation algorithm (DeepMSA2) with the end-to-end modeling module from AlphaFold2-Multimer. For the 38 complex targets, DMFold-Multimer generated models with an average TM-score of 0.83 and Interface Contact Score of 0.60, both significantly higher than those of competing complex prediction tools. Our analyses on complexes highlighted the critical role played by MSA generating, ranking, and pairing in protein complex structure prediction. We also discuss future room for improvement in the areas of viral protein modeling and complex model ranking.

Petascale Homology Search for Structure Prediction

The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, we conducted a petabase-scale search of the Sequence Read Archive (SRA), resulting in gigabytes of aligned homologs for CASP15 targets. These were merged with default MSAs produced by ColabFold-search and provided to ColabFold-predict. By using SRA data, we achieved highly accurate predictions (GDT_TS > 70) for 66% of the non-easy targets, whereas using ColabFold-search default MSAs scored highly in only 52%. Next, we tested the effect of deep homology search and ColabFold's advanced features, such as more recycles, on prediction accuracy. While SRA homologs were most significant for improving ColabFold's CASP15 ranking from 11th to 3rd place, other strategies contributed too. We analyze these in the context of existing strategies to improve prediction.

Microbiome-based enrichment pattern mining has enabled a deeper understanding of the biome-species-function relationship

Microbes live in diverse habitats (i.e. biomes), yet their species and genes were biome-specific, forming enrichment patterns. These enrichment patterns have mirrored the biome-species-function relationship, which is shaped by ecological and evolutionary principles. However, a grand picture of these enrichment patterns, as well as the roles of external and internal factors in driving these enrichment patterns, remain largely unexamined. In this work, we have examined the enrichment patterns based on 1705 microbiome samples from four representative biomes (Engineered, Gut, Freshwater, and Soil). Moreover, an "enrichment sphere" model was constructed to elucidate the regulatory principles behind these patterns. The driving factors for this model were revealed based on two case studies: (1) The copper-resistance genes were enriched in Soil biomes, owing to the copper contamination and horizontal gene transfer. (2) The flagellum-related genes were enriched in the Freshwater biome, due to high fluidity and vertical gene accumulation. Furthermore, this enrichment sphere model has valuable applications, such as in biome identification for metagenome samples, and in guiding 3D structure modeling of proteins. In summary, the enrichment sphere model aims towards creating a bluebook of the biome-species-function relationships and be applied in many fields.

Designing of thiazolidinones against chicken pox, monkey pox, and hepatitis viruses: A computational approach

Computational designing of four different series (D-G) of thiazolidinone was done starting from different amines which was further condensed with various aldehydes. These underwent in silico molecular investigations for density functional theory (DFT), molecular docking, and absorption, distribution metabolism, excretion, and toxicity (ADMET) studies. The different electrochemical parameters of the compounds are predicted using quantum mechanical modeling approach with Gaussian. The docking software was used to dock the compounds against choosing PDB file for chickenpox, human immunodeficiency, hepatitis, and monkeypox virus as 1OSN, 1VZV, 6VLK, 1RTD, 3I7H, 3TYV, 4JU3, and 4QWO, respectively. The molecular interactions were visualized with discovery studio and maximum binding affinity was observed with D8 compounds against 4QWO (-13.383 kcal/mol) while for compound D5 against 1VZV which was -12.713 kcal/mol. Swiss ADME web tool was used to assess the drug-likeness of the designed compounds under consideration, and it is concluded that these molecules had a drug-like structure with almost zero violations.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Characterization of Treponema denticola Major Surface Protein (Msp) by Deletion Analysis and Advanced Molecular Modeling

Treponema denticola, a keystone pathogen in periodontitis, is a model organism for studying Treponema physiology and host-microbe interactions. Its major surface protein Msp forms an oligomeric outer membrane complex that binds fibronectin, has cytotoxic pore-forming activity, and disrupts several intracellular processes in host cells. T. denticola msp is an ortholog of the Treponema pallidum tprA to -K gene family that includes tprK, whose remarkable in vivo hypervariability is proposed to contribute to T. pallidum immune evasion. We recently identified the primary Msp surface-exposed epitope and proposed a model of the Msp protein as a β-barrel protein similar to Gram-negative bacterial porins. Here, we report fine-scale Msp mutagenesis demonstrating that both the N and C termini as well as the centrally located Msp surface epitope are required for native Msp oligomer expression. Removal of as few as three C-terminal amino acids abrogated Msp detection on the T. denticola cell surface, and deletion of four residues resulted in complete loss of detectable Msp. Substitution of a FLAG tag for either residues 6 to 13 of mature Msp or an 8-residue portion of the central Msp surface epitope resulted in expression of full-length Msp but absence of the oligomer, suggesting roles for both domains in oligomer formation. Consistent with previously reported Msp N-glycosylation, proteinase K treatment of intact cells released a 25 kDa polypeptide containing the Msp surface epitope into culture supernatants. Molecular modeling of Msp using novel metagenome-derived multiple sequence alignment (MSA) algorithms supports the hypothesis that Msp is a large-diameter, trimeric outer membrane porin-like protein whose potential transport substrate remains to be identified. IMPORTANCE The Treponema denticola gene encoding its major surface protein (Msp) is an ortholog of the T. pallidum tprA to -K gene family that includes tprK, whose remarkable in vivo hypervariability is proposed to contribute to T. pallidum immune evasion. Using a combined strategy of fine-scale mutagenesis and advanced predictive molecular modeling, we characterized the Msp protein and present a high-confidence model of its structure as an oligomer embedded in the outer membrane. This work adds to knowledge of Msp-like proteins in oral treponemes and may contribute to understanding the evolutionary and potential functional relationships between T. denticola Msp and the orthologous T. pallidum Tpr proteins.

LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation

Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function annotation, which integrates newly developed deep learning threading methods. For the first time, we have extended LOMETS3 to handle multi-domain proteins and to construct full-length models with gradient-based optimizations. Starting from a FASTA-formatted sequence, LOMETS3 performs four steps of domain boundary prediction, domain-level template identification, full-length template/model assembly and structure-based function prediction. The output of LOMETS3 contains (i) top-ranked templates from LOMETS3 and its component threading programs, (ii) up to 5 full-length structure models constructed by L-BFGS (limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm) optimization, (iii) the 10 closest Protein Data Bank (PDB) structures to the target, (iv) structure-based functional predictions, (v) domain partition and assembly results, and (vi) the domain-level threading results, including items (i)–(iii) for each identified domain. LOMETS3 was tested in large-scale benchmarks and the blind CASP14 (14th Critical Assessment of Structure Prediction) experiment, where the overall template recognition and function prediction accuracy is significantly beyond its predecessors and other state-of-the-art threading approaches, especially for hard targets without homologous templates in the PDB. Based on the improved developments, LOMETS3 should help significantly advance the capability of broader biomedical community for template-based protein structure and function modelling.

How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives

It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.

Using metagenomic data to boost protein structure prediction and discovery

Over the past decade, metagenomic sequencing approaches have been providing an ever-increasing amount of protein sequence data at an astonishing rate. These constitute an invaluable source of information which has been exploited in various research fields such as the study of the role of the gut microbiota in human diseases and aging. However, only a small fraction of all metagenomic sequences collected have been functionally or structurally characterized, leaving much of them completely unexplored. Here, we review how this information has been used in protein structure prediction and protein discovery. We begin by presenting some widely used metagenomic databases and analyze in detail how metagenomic data has contributed to the impressive improvement in the accuracy of structure prediction methods in recent years. We then examine how metagenomic information can be exploited to annotate protein sequences. More specifically, we focus on the role of metagenomes in the discovery of enzymes and new CRISPR-Cas systems, and in the identification of antibiotic resistance genes. With this review, we provide an overview of how metagenomic data is currently revolutionizing our understanding of protein science.