Crystallography at low resolution must determine the atomic model from less experimental observations, which is challenging in the absence of a model. In addition, model bias is more severe when independent experimental data are scarce. Our methods solve the phase problem by combining the location of accurate model fragments using Phaser with density modification and interpretation of the resulting maps using SHELXE. From a partial, correct structure, the density modification process and the stereochemical constraints draw the rest of the structure, validating the result. This same principle is now exploited at low resolution. Coiled coils are important, ubiquitous structures but notoriously difficult to phase and to predict. Both correct solutions and incorrect ones are poorly discriminated by the crystallographic figures of merit as long as helices are correctly oriented. We incorporate coiled-coil verification, designed to set up competing, incompatible structural hypotheses to probe both the results and establish the power of the data to discriminate them. Efficiency of coiled-coil phasing and validation in test cases from 3 to 4 Å is demonstrated in ARCIMBOLDO_LITE, placing single helices, and in ARCIMBOLDO_SHREDDER, with fragments derived from AlphaFold models. SHELXE tracing at low resolution has been enhanced, maintaining its local character but extending the environment assessment. For non-helical structures, verification is demonstrated in the fragment location process. Its use is exemplified with the solution of the VSR1 structure at 3.5 Å, depending on LLG optimization and the emergence of new features in the electron density. Relying on verification, we have extended the use of the ARCIMBOLDO software to low resolution.

BACKGROUND: The ubiquitously expressed Guanine nucleotide exchange factor, RAPGEF1 (C3G), is essential for early development of mouse embryos. It functions to regulate gene expression and cytoskeletal reorganization, thereby controlling cell proliferation and differentiation. While multiple transcripts have been predicted, their expression in mouse tissues has not been investigated in detail.
RESULTS: Full length RAPGEF1 isoforms primarily arise due to splicing at two hotspots, one involving exon-3, and the other involving exons 12-14 incorporating amino acids immediately following the Crk binding region of the protein. These isoforms vary in expression across embryonic and adult organs. We detected the presence of unannotated, and unpredicted transcripts with incorporation of cassette exons in various combinations, specifically in the heart, brain, testis and skeletal muscle. Isoform switching was detected as myocytes in culture and mouse embryonic stem cells were differentiated to form myotubes, and embryoid bodies respectively. The cassette exons encode a serine-rich polypeptide chain, which is intrinsically disordered, and undergoes phosphorylation. In silico structural analysis using AlphaFold indicated that the presence of cassette exons alters intra-molecular interactions, important for regulating catalytic activity. LZerD based docking studies predicted that the isoforms with one or more cassette exons differ in interaction with their target GTPase, RAP1A.
CONCLUSIONS: Our results demonstrate the expression of novel RAPGEF1 isoforms, and predict cassette exon inclusion as an additional means of regulating RAPGEF1 activity in various tissues and during differentiation.

Accurate protein solubility prediction is crucial in screening suitable candidates for food application. Existing models often rely only on sequences, overlooking important structural details. In this study, a regression model for protein solubility was developed using both the sequences and predicted structures of 2983 E. coli proteins. The sequence and structural level properties of the proteins were bioinformatically extracted and subjected to multilayer perceptron (MLP). Moreover, residue level features and contact maps were utilized to construct a graph convolutional network (GCN). The out-of-fold predictions of the two models were combined and fed into multiple meta-regressors to create a stacking model. The stacking model with support vector regressor (SVR) achieved R2 of 0.502 and 0.468 on test and external validation datasets, respectively, displaying higher performance compared to existing regression models. Based on the improved performance compared to its based models, the stacking model effectively captured the strength of its base models as well as the significance of the different features used. Furthermore, the model\'s transferability was indirectly validated on a dataset of seed storage proteins using Osborne definition as well as on a case study using molecular dynamic simulation, showing potential for application beyond microbial proteins to food and agriculture-related ones.

Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model\'s capabilities and limitations.

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein-protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation.

Endosomal membrane trafficking is mediated by specific protein coats and formation of actin-rich membrane domains. The Retromer complex coordinates with sorting nexin (SNX) cargo adaptors including SNX27, and the SNX27-Retromer assembly interacts with the Wiskott-Aldrich syndrome protein and SCAR homolog (WASH) complex which nucleates actin filaments establishing the endosomal recycling domain. Crystal structures, modeling, biochemical, and cellular validation reveal how the FAM21 subunit of WASH interacts with both Retromer and SNX27. FAM21 binds the FERM domain of SNX27 using acidic-Asp-Leu-Phe (aDLF) motifs similar to those found in the SNX1 and SNX2 subunits of the ESCPE-1 complex. Overlapping FAM21 repeats and a specific Pro-Leu containing motif bind three distinct sites on Retromer involving both the VPS35 and VPS29 subunits. Mutation of the major VPS35-binding site does not prevent cargo recycling; however, it partially reduces endosomal WASH association indicating that a network of redundant interactions promote endosomal activity of the WASH complex. These studies establish the molecular basis for how SNX27-Retromer is coupled to the WASH complex via overlapping and multiplexed motif-based interactions required for the dynamic assembly of endosomal membrane recycling domains.

Selective autophagic degradation of cellular components has been shown to be mediated by the interaction of LIR motif-containing proteins with ATG8-family proteins. Here, we present a detailed methodology for the in silico evaluation of potential binding between LIR motif-containing proteins and ATG8-family proteins. We visualize AlphaFold-predicted protein complexes using PyMOL to assess potential interactions, providing an effective computational tool for this purpose.

Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies.

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The docking of an acyl carrier protein (ACP) domain with a downstream ketosynthase (KS) domain in each module of a polyketide synthase (PKS) helps ensure accurate biosynthesis. If the polyketide chain bound to the ACP has been properly modified by upstream processing enzymes and is compatible with gatekeeping residues in the KS tunnel, a transacylation reaction can transfer it from the 18.1-Å phosphopantetheinyl arm of the ACP to the reactive cysteine of the KS. AlphaFold-Multimer predicts a general interface for these transacylation checkpoints. Half of the solutions obtained for 50 ACP/KS pairs show the KS motif TxLGDP forming the first turn of an α-helix, as in reported structures, while half show it forming a type I β-turn not previously observed. Solutions with the latter conformation may represent how these domains are relatively positioned during the transacylation reaction, as the entrance to the KS active site is relatively open and the phosphopantetheinylated ACP serine and the reactive KS cysteine are relatively closer-17.2 versus 20.9 Å, on average. To probe the predicted interface, 20 mutations were made to KS surface residues within the model triketide lactone synthase P1-P6-P7. The activities of these mutants are consistent with the proposed interface.