The risk of pandemics is increasing as global population growth and interconnectedness accelerate. Understanding the structural basis of protein-protein interactions between pathogens and hosts is critical for elucidating pathogenic mechanisms and guiding treatment or vaccine development. Despite 21,064 experimentally supported human-pathogen interactions in the HPIDB, only 52 have resolved structures in the PDB, representing just 0.2%. Advances in protein complex structure prediction, such as AlphaFold, now enable highly accurate modelling of heterodimeric complexes, though their application to host-pathogen interactions, which have distinct evolutionary dynamics, remains underexplored. Here, we investigate the structural protein-protein interaction network between humans and ten pathogens, predicting structures for 9,452 interactions, only 10 of which have known structures. We identify 30 interactions with an expected TM-score ≥0.9, tripling the structural coverage in these networks. A detailed analysis of the Francisella tularensis dihydroprolyl dehydrogenase (IPD) complex with human immunoglobulin kappa constant (IGKC) using homology modelling and native mass spectrometry confirms a predicted 1:2:1 heterotetramer, suggesting potential roles in immune evasion. These findings highlight the transformative potential of structure prediction for rapidly advancing vaccine and drug development against novel pathogenic targets.

Structure prediction technology has transformed protein design, yet key challenges remain, particularly in designing novel functions. Many proteins function through interactions with other proteins, making the rational design of these interactions a central problem. While most efforts focus on large, stable proteins, shorter peptides offer advantages such as lower manufacturing costs, reduced steric hindrance, and improved cell permeability when cyclised. However, their flexibility and limited structural data make them difficult to design. Here, we introduce EvoBind2, a method for designing novel linear and cyclic peptide binders of varying lengths using only the sequence of a target protein. Unlike existing approaches, EvoBind2 does not require prior knowledge of binding sites or predefined binder lengths, making it a fully blind design process. For one target protein, we demonstrate that linear and cyclic peptide binders of different lengths can be designed in a single shot, and adversarial designs can be avoided through orthogonal in silico evaluation.

Structure prediction of protein complexes has improved significantly with AlphaFold2 and AlphaFold-multimer (AFM), but only 60% of dimers are accurately predicted. Here, we learn a bias to the MSA representation that improves the predictions by performing gradient descent through the AFM network. We demonstrate the performance on seven difficult targets from CASP15 and increase the average MMscore to 0.76 compared to 0.63 with AFM. We evaluate the procedure on 487 protein complexes where AFM fails and obtain an increased success rate (MMscore>0.75) of 33% on these difficult targets. Our protocol, AFProfile, provides a way to direct predictions towards a defined target function guided by the MSA. We expect gradient descent over the MSA to be useful for different tasks.

Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins with conformations most likely to exist in the PDB. However, almost all protein structures with multiple conformations represented in the PDB have been used while training these models. Therefore, it is unclear whether alternative protein conformations can be genuinely predicted using these networks, or if they are simply reproduced from memory. Here, we train a structure prediction network, Cfold, on a conformational split of the PDB to generate alternative conformations. Cfold enables efficient exploration of the conformational landscape of monomeric protein structures. Over 50% of experimentally known nonredundant alternative protein conformations evaluated here are predicted with high accuracy (TM-score > 0.8).

Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly from sequence information. We find that classical docking methods are still superior, but depend upon having crystal structures of the target protein. In addition to predicting flexible all-atom structures, predicted confidence metrics (plDDT) can be used to select accurate predictions as well as to distinguish between strong and weak binders. The advances presented here suggest that the goal of AI-based drug discovery is one step closer, but there is still a way to go to grasp the complexity of protein-ligand interactions fully. Umol is available at: https://github.com/patrickbryant1/Umol.

Most breast cancer heritability is unexplained. We hypothesized that analysis of unrelated familial cases in a GWAS context could enable the identification of novel susceptibility loci. In order to examine the association of a haplotype with breast cancer risk, we performed a genome-wide haplotype association study using a sliding window analysis of window sizes 1–25 SNPs in 650 familial invasive breast cancer cases and 5021 controls. We identified five novel risk loci on 9p24.3 (OR 3.4; p 4.9 × 10⁻¹¹), 11q22.3 (OR 2.4; p 5.2 × 10⁻⁹), 15q11.2 (OR 3.6; p 2.3 × 10⁻⁸), 16q24.1 (OR 3; p 3 × 10⁻⁸) and Xq21.31 (OR 3.3; p 1.7 × 10⁻⁸) and confirmed three well-known loci on 10q25.13, 11q13.3, and 16q12.1. In total, 1593 significant risk haplotypes and 39 risk SNPs were distributed on the eight loci. In comparison with unselected breast cancer cases from a previous study, the OR was increased in the familial analysis in all eight loci. Analyzing familial cancer cases and controls enabled the identification of novel breast cancer susceptibility loci.

Recent developments in the structure prediction of protein complexes have resulted in accuracies rivalling experimental methods in many cases. The high accuracy is mainly observed in dimeric complexes and other problems such as protein disorder and predicting the structure of host-pathogen in-teractions remain. This review highlights the foundation for current accurate structure prediction of protein complexes and possible ways to address the remaining limitations.

Objective  Lynch syndrome is caused by germline mutations in the mismatch repair (MMR) genes, such as the PMS2 gene, and is characterised by a familial accumulation of colorectal cancer. The penetrance of cancer in PMS2 carriers is still not fully elucidated as a colorectal cancer risk has been shown to vary between PMS2 carriers, suggesting the presence of risk modifiers.

Methods  Whole exome sequencing was performed in a Swedish family carrying a PMS2 missense mutation [c.2113G>A, p.(Glu705Lys)]. Thirteen genetic sequence variants were further selected and analysed in a case-control study (724 cases and 711 controls).

Results  The most interesting variant was an 18 bp deletion in gene BAG1. BAG1 has been linked to colorectal tumour progression with poor prognosis and is thought to promote colorectal tumour cell survival through increased NF-κB activity.

Conclusions  We conclude the genetic architecture behind the incomplete penetrance of PMS2 is complicated and must be assessed in a genome wide manner using large families and multifactorial analysis.

The computational design of peptide binders towards a specific protein interface can aid diagnostic and therapeutic efforts. Here, we design peptide binders by combining the known structural space searched with Foldseek, the protein design method ESM-IF1, and AlphaFold2 (AF) in a joint framework. Foldseek generates backbone seeds for a modified version of ESM-IF1 adapted to protein complexes. The resulting sequences are evaluated with AF using an MSA representation for the receptor structure and a single sequence for the binder. We show that AF can accurately evaluate protein binders and that our bind score can select these (ROC AUC = 0.96 for the heterodimeric case). We find that designs created from seeds with more contacts per residue are more successful and tend to be short. There is a relationship between the sequence recovery in interface positions and the plDDT of the designs, where designs with >= 80% recovery have an average plDDT of 84 compared to 55 at 0%. Designed sequences have 60% higher median plDDT values towards intended receptors than non-intended ones. Successful binders (predicted interface RMSD <= 2 angstrom) are designed towards 185 (6.5%) heteromeric and 42 (3.6%) homomeric protein interfaces with ESM-IF1 compared with 18 (1.5%) using ProteinMPNN from 100 samples. Designing peptides that bind to specific protein targets is crucial for peptidic drug development, however, traditional computer-aided binder design is outperformed by AlphaFold2. Here, the authors develop a peptide binder designing tool by combining Foldseek, ESM-IF1 and AlphaFold2 to increase the success rate.

Cellular functions are governed by molecular machines that assemble through protein-protein interactions. Their atomic details are critical to studying their molecular mechanisms. However, fewer than 5% of hundreds of thousands of human protein interactions have been structurally characterized. Here we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure. We identify interface residues harboring disease mutations, suggesting potential mechanisms for pathogenic variants. Groups of interface phosphorylation sites show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple protein interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies helping to expand our understanding of human cell biology.