protein functions
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O-linked glycosylation, the greatest form of post-translational modifications, plays a key role in regulating the majority of physiological processes. It is, therefore, not surprising that abnormal O-linked glycosylation has been related to several human diseases. Recently, GALNT2, which encodes the GalNAc-transferase 2 involved in the first step of O-linked glycosylation, has attracted great attention as a possible player in many highly prevalent human metabolic diseases, including atherogenic dyslipidemia, type 2 diabetes and obesity, all clustered on the common ground of insulin resistance. Data available both in human and animal models point to GALNT2 as a molecule that shapes the risk of the aforementioned abnormalities affecting diverse protein functions, which eventually cause clinically distinct phenotypes (a typical example of pleiotropism). Pathways linking GALNT2 to dyslipidemia and insulin resistance have been partly identified, while those for type 2 diabetes and obesity are yet to be understood. Here, we will provide a brief overview on the present knowledge on GALNT2 function and dysfunction and propose novel insights on the complex pathogenesis of the aforementioned metabolic diseases, which all impose a heavy burden for patients, their families and the entire society.

Motivation: Protein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50,000 classes and a large set of formal axioms. Predicting the functions of proteins is one of the key challenges in computational biology and a variety of machine learning methods have been developed for this purpose. However, these methods usually require significant amount of training data and cannot make predictions for GO classes which have only few or no experimental annotations. Results: We developed DeepGOZero, a machine learning model which improves predictions for functions with no or only a small number of annotations. To achieve this goal, we rely on a model-theoretic approach for learning ontology embeddings and combine it with neural networks for protein function prediction. DeepGOZero can exploit formal axioms in the GO to make zero-shot predictions, i.e., predict protein functions even if not a single protein in the training phase was associated with that function. Furthermore, the zero-shot prediction method employed by DeepGOZero is generic and can be applied whenever associations with ontology classes need to be predicted. Availability: http://github.com/bio-ontology-research-group/deepgozero

Recombinant proteins are ubiquitously applied in fields like research, pharma, diagnostics or the chemical industry. To provide the full range of useful proteins, novel expression hosts need to be established for proteins that are not sufficiently produced by the standard platform organisms. Unconventional secretion in the fungal model Ustilago maydis is an attractive novel option for export of heterologous proteins without N-glycosylation using chitinase Cts1 as a carrier. Recently, a novel factor essential for unconventional Cts1 secretion termed Jps1 was identified. Here, we show that Jps1 is unconventionally secreted using a fusion to bacterial β-glucuronidase as an established reporter. Interestingly, the experiment also demonstrates that the protein functions as an alternative carrier for heterologous proteins, showing about 2-fold higher reporter activity than the Cts1 fusion in the supernatant. In addition, Jps1-mediated secretion even allowed for efficient export of functional firefly luciferase as a novel secretion target which could not be achieved with Cts1. As an application for a relevant pharmaceutical target, export of functional bi-specific synthetic nanobodies directed against the SARS-CoV2 spike protein was demonstrated. The establishment of an alternative efficient carrier thus constitutes an excellent expansion of the existing secretion platform.

Understanding the mechanism of functional protein dynamics is critical to understanding protein functions. Reaction coordinates is a central topic in protein dynamics and the grail is to find the one-dimensional reaction coordinate that can fully determine the value of committor (i.e. the reaction probability in configuration space) for any protein configuration. We present a powerful new method that can, for the first time, identify the rigorous one-dimensional reaction coordinate in complex molecules. This one-dimensional reaction coordinate is determined by a fundamental mechanical operator--the generalized work functional. This method only requires modest computational cost and can be readily applied to large molecules. Most importantly, the generalized work functional is the physical origin of the collectivity in functional protein dynamics and provides a tentative roadmap that connects the structure of a protein to its function.

Background: SARS-CoV-2 virus is a highly transmissible pathogen that causes COVID-19. The outbreak originated in Wuhan, China in December 2019. A number of nonsynonymous mutations located at different SARS-CoV-2 proteins have been reported by multiple studies. However, there are limited computational studies on the biological impacts of these mutations on the structure and function of the proteins.   Methods: In our study nonsynonymous mutations of the SARS-CoV-2 genome and their frequencies were identified from 30,229 sequences. Subsequently, the effects of the top 10 nonsynonymous mutations of different SARS-CoV-2 proteins were analyzed using bioinformatics tools including co-mutation analysis, prediction of the protein structure stability and flexibility analysis, and prediction of the protein functions.   Results: A total of 231 nonsynonymous mutations were identified from 30,229 SARS-CoV-2 genome sequences. The top 10 nonsynonymous mutations affecting nine amino acid residues were ORF1a nsp5 P108S, ORF1b nsp12 P323L and A423V, S protein N501Y and D614G, ORF3a Q57H, N protein P151L, R203K and G204R. Many nonsynonymous mutations showed a high concurrence ratio, suggesting these mutations may evolve together and interact functionally. Our result showed that ORF1a nsp5 P108S, ORF3a Q57H and N protein P151L mutations may be deleterious to the function of SARS-CoV-2 proteins. In addition, ORF1a nsp5 P108S and S protein D614G may destabilize the protein structures while S protein D614G may have a more open conformation compared to the wild type.   Conclusion: The biological consequences of these nonsynonymous mutations of SARS-CoV-2 proteins should be further validated by in vivo and in vitro experimental studies in the future.

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+, Mg2+, Na+, and K+). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the Sn and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

Heme is located in the active site of proteins and has diverse and important biological functions, such as electron transfer and oxygen transport and/or storage. The distortion of heme porphyrin is considered an important factor for the diverse functions of heme because it correlates with the physical properties of heme, such as oxygen affinity and redox potential. Therefore, clarification of the relationship between heme distortion and the protein environment is crucial in protein science. Here, we analyzed the fluctuation in heme distortion in the protein environment for hemoglobin and myoglobin using molecular dynamics (MD) simulations and quantum mechanical (QM) calculations. We also investigated the protein structures of hemoglobin and myoglobin stored in Protein Data Bank and found that heme is distorted along the doming mode, which correlates with its oxygen affinity, more prominently in the protein environment than in the isolated state, and the magnitude of distortion is different between hemoglobin and myoglobin. This tendency was also observed in the results of MD simulations and QM calculations. These results suggest that heme distortion is affected by its protein environment and fluctuates around its fitted conformation, leading to physical properties that are appropriate for protein functions.

DNA replication, gene expression, and genomic repair all require precise coordination of the many proteins that interact with DNA. This includes the histones as well as their chaperones.

Short-range, non-covalent interactions between amino acid residues determine protein structures and contribute to protein functions in diverse ways. The interactions of the thioether of methionine with the aromatic rings of tyrosine, tryptophan, and/or phenylalanine has long been discussed and such interactions are favorable on the order of 1–3 kcal mol−1. Here, we carry out a new bioinformatics survey of known protein structures where we assay the propensity of three aromatic residues to localize around the [-CH2-S-CH3] of methionine. We term these groups “3-bridge clusters”. A dataset consisting of 33,819 proteins with less than 90% sequence identity was analyzed and such clusters were found in 4093 structures (or 12% of the non-redundant dataset). All sub-classes of enzymes were represented. A 3D coordinate analysis shows that most aromatic groups localize near the CH2 and CH3 of methionine. Quantum chemical calculations support that the 3-bridge clusters involve a network of interactions that involve the Met-S, Met-CH2, Met-CH3, and the π systems of nearby aromatic amino acid residues. Selected examples of proposed functions of 3-bridge clusters are discussed.