Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model.

Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles continue to be a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold (AF), this study computationally examines the entire UniProt database to create a robust dataset of knotted and unknotted proteins. Utilizing this dataset, we develop a machine learning (ML) model capable of accurately predicting the presence of knots in protein structures solely from their amino acid sequences. We tested the model's capabilities on 100 proteins whose structures had not yet been predicted by AF and found agreement with our local prediction in 92% cases. From the point of view of structural biology, we found that all potentially knotted proteins predicted by AF can be classified only into 17 families. This allows us to discover the presence of unknotted proteins in families with a highly conserved knot. We found only three new protein families: UCH, DUF4253, and DUF2254, that contain both knotted and unknotted proteins, and demonstrate that deletions within the knot core could potentially account for the observed unknotted (trivial) topology. Finally, we have shown that in the majority of knotted families (11 out of 15), the knotted topology is strictly conserved in functional proteins with very low sequence similarity. We have conclusively demonstrated that proteins AF predicts as unknotted are structurally accurate in their unknotted configurations. However, these proteins often represent nonfunctional fragments, lacking significant portions of the knot core (amino acid sequence).

AlphaFold predicts novel human proteins with knots.

The fact that proteins can have their chain formed in a knot is known for almost 30 years. However, as they are not common, only a fraction of such proteins is available in the Protein Data Bank. It was not possible to assess their importance and versatility up until now because we did not have access to the whole proteome of an organism, let alone a human one. The arrival of efficient machine learning methods for protein structure prediction, such as AlphaFold and RoseTTaFold, changed that. We analyzed all proteins from the human proteome (over 20,000) determined with AlphaFold in search for knots and found them in less than 2% of the structures. Using a variety of methods, including homolog search, clustering, quality assessment, and visual inspection, we determined the nature of each of the knotted structures and classified it as either knotted, potentially knotted, or an artifact, and deposited all of them in a database available at: https://knotprot.cent.uw.edu.pl/alphafold. Overall, we found 51 credible knotted proteins (0.2% of human proteome). The set of potentially knotted structures includes a new complex type of a knot not reported in proteins yet. That knot type, denoted 63 in mathematical notation, would necessitate a more complex folding path than any knotted protein characterized to date.

New lung mass in a patient with granulomatosis with polyangiitis.

Granulomatosis with polyangiitis (GPA) is a potentially lethal ANCA-associated small-vessel vasculitis characterized by a typical triad of upper respiratory tract, lung, and kidney involvement. Lung involvement in GPA occurs in 25-80% of cases. The most common radiographic and computed tomography (CT) abnormalities of pulmonary GPA are lung nodules and masses, very often multiple and with cavitation. As there are various clinical presentations, the diagnosis of GPA can be challenging, and the illness is difficult to distinguish from other diseases such as infection or malignancy. Following the improved survival rates in patients with GPA, there is accumulating evidence to suggest an increased occurrence of different types of cancer. Exposure to cyclophosphamide seems to be one of its main causes. We present the case of a patient with chronic GPA who was hospitalized owing to a new infiltrate in the lung, suggesting relapse of the disease, and finally diagnosed with small cell lung cancer. Data regarding lung cancer in GPA patients are limited. While there are some case reports and short case series in the literature, there are no detailed data regarding an association between CYC exposure and lung cancer development in vasculitis. It is necessary to consider the causes of pulmonary masses other than a GPA relapse. Bronchoscopy with biopsy and histopathological examination are crucial in proper differential diagnosis. GPA patients require long-term follow-up to monitor for the development of complications during treatment.

Changed phagocytic activity and pattern of Fcγ and complement receptors on blood monocytes in sarcoidosis.

We have recently revealed that mycobacterial heat shock proteins (Mtb-hsp), involved in forming of immune complexes (CIs), can induce immune response in sarcoidosis (SA). The complexemia may result from inappropriate phagocytosis and clearance of CIs by monocytes with following persistent antigenemia and granuloma formation. Because an aberrant expression of receptors for Fc fragment of immunoglobulin G (FcγR) and complement receptors (CR) on monocytes can be involved in this process, we have evaluated the expression of FcγRI (CD64), FcγRII (CD32), FcγRIII (CD16) and CR1 (CD35), CR3 (CD11b), CR4 (CD11c) receptors on blood CD14(+) monocytes and its phagocytic activity in 24 patients with SA and 20 healthy volunteers using flow cytometry. We found significantly increased expression of all examined FcγR and decreased expression of CD35 and CD11c on CD14(+) monocytes in SA patients vs controls. Significantly increased percentage of CD14(+)CD16(+)CD35(-), CD14(+)CD64(+)CD35(+), CD14(+)CD64(+)CD11b(+), CD14(+)CD64(+)CD11c(+) and decreased of CD14(+)CD32(-)CD35(+), CD14(+)CD32(-)CD11b(+), CD14(+)CD32(-)CD11c(+) monocytes' phenotypes was revealed in SA. The total number and percentage of phagocyting monocytes was significantly increased in SA as compared with controls. In conclusion, altered expression of FcγR and CR on CD14(+) monocytes and its increased phagocytic activity may be responsible for high antigen load, persistent antigenemia and immunocomplexemia in SA patients.

Library of local descriptors models the core of proteins accurately.

In this article, we present a novel approach to describing proteins based on multifragment structure motifs called local descriptors. We collect structurally similar descriptors in groups to construct a compact library of groups of descriptors. To demonstrate its feasibility for a wide spectrum of applications, ranging from structure comparison and analysis to structure prediction, it is critical to show the ability of groups from our library to reproduce proteins accurately. We show that this library describes all local 3D structure patterns occurring in the core of proteins and present an algorithm for reconstruction of accurate global 3D structures. Moreover, we show that the sequence of motifs used in such a construction correlates significantly with the amino acid sequence of the considered protein. Finally, we present how our library may be successfully used for predicting protein sequence based on the structure.

Ideal amino acid exchange forms for approximating substitution matrices.

We have analyzed 29 published substitution matrices (SMs) and five statistical protein contact potentials (CPs) for comparison. We find that popular, 'classical' SMs obtained mainly from sequence alignments of globular proteins are mostly correlated by at least a value of 0.9. The BLOSUM62 is the central element of this group. A second group includes SMs derived from alignments of remote homologs or transmembrane proteins. These matrices correlate better with classical SMs (0.8) than among themselves (0.7). A third group consists of intermediate links between SMs and CPs - matrices and potentials that exhibit mutual correlations of at least 0.8. Next, we show that SMs can be approximated with a correlation of 0.9 by expressions c(0) + x(i)x(j) + y(i)y(j) + z(i)z(j), 1

A new approach to the assessment of the quality of predictions of transcription factor binding sites.

In this paper, we describe a novel method called Secondary Verification which assesses the quality of predictions of transcription factor binding sites. This method incorporates a distribution of prediction scores over positive examples (i.e. the actual binding sites) and is shown to be superior to p-value, routinely used statistical significance assessment, which uses only a distribution of prediction scores over background sequences. We also discuss how to integrate both distributions into a framework called Secondary Verification Assessment method which evaluates the quality of a model of a transcription factor. Based on that we create a hybrid representation of a transcription factor: we select the description (with or without dependencies) which is best for the transcription factor considered.