Here, we comment on the steadily increasing body of data showing that proteins with specificity actually bind ligands of diverse shapes, sizes, and composition. Such a phenomenon is not surprising ...when one considers that binding is a dynamic process with populations in equilibrium and that the shape of the binding site is strongly influenced by the molecular partner. It derives implicitly from the concept of populations. All proteins, specific and nonspecific, exist in ensembles of substates. If the library of ligands in solution is large enough, favorably matching ligands with altered shapes and sizes can be expected to bind, with a redistribution of the protein populations. Point mutations at spatially distant sites may exert large conformational rearrangements and hinge effects, consistent with mutations away from the binding site leading to population shifts and (cross‐)drug resistance. A similar effect is observed in protein superfamilies, in which different sequences with similar topologies display similar large‐scale dynamic motions. The hinges are frequently at analogous sites, yet with different substrate specificity. Similar topologies yield similar conformational isomers, although with different distributions of population times, owing to the change in the conditions, that is, the change in the sequences. In turn, different distributions relate to binding of different sizes and shapes. Hence, the binding site shape and size are defined by the ligand. They are not independent entities of fixed proportions and cannot be analyzed independently of the binding partner. Such a proposition derives from viewing proteins as dynamic distributions, presenting to the incoming ligands a range of binding site shapes. It illustrates how presumably specific binding molecules can bind multiple ligands. In terms of drug design, the ability of a single receptor to recognize many dissimilar ligands shows the need to consider more diverse molecules. It provides a rationale for higher affinity inhibitors that are not derived from substrates at their transition states and indicates flexible docking schemes.
Sulfate-reducing bacteria (SRB) are obligate anaerobes that can couple their growth to the reduction of sulfate. Despite the importance of SRB to global nutrient cycles and their damage to the ...petroleum industry, our molecular understanding of their physiology remains limited. To systematically provide new insights into SRB biology, we generated a randomly barcoded transposon mutant library in the model SRB
Hildenborough (DvH) and used this genome-wide resource to assay the importance of its genes under a range of metabolic and stress conditions. In addition to defining the essential gene set of DvH, we identified a conditional phenotype for 1,137 non-essential genes. Through examination of these conditional phenotypes, we were able to make a number of novel insights into our molecular understanding of DvH, including how this bacterium synthesizes vitamins. For example, we identified DVU0867 as an atypical L-aspartate decarboxylase required for the synthesis of pantothenic acid, provided the first experimental evidence that biotin synthesis in DvH occurs
a specialized acyl carrier protein and without methyl esters, and demonstrated that the uncharacterized dehydrogenase DVU0826:DVU0827 is necessary for the synthesis of pyridoxal phosphate. In addition, we used the mutant fitness data to identify genes involved in the assimilation of diverse nitrogen sources and gained insights into the mechanism of inhibition of chlorate and molybdate. Our large-scale fitness dataset and RB-TnSeq mutant library are community-wide resources that can be used to generate further testable hypotheses into the gene functions of this environmentally and industrially important group of bacteria.
Background: The cost of heart failure hospitalizations in the US alone is over USD 10 billion per year. Over 4 million Americans are hospitalized every year due to heart failure (HF), with a median ...length of stay of 4 days and an in-hospital mortality rate that exceeds 5%. Hospitalizations of patients with HF can be prevented by early detection of lung congestion. Our study assessed a new contact-free optical medical device used for the early detection of lung congestion. Methods: The Gili system is an FDA-cleared device used for measuring chest motion vibration data. Lung congestion in the study was assessed clinically and verified via two cardiologists. An algorithm was developed using machine learning techniques, and cross-validation of the findings was performed to estimate the accuracy of the algorithm. Results: A total of 227 patients were recruited (101 cases vs. 126 controls). The sensitivity and specificity for the device in our study were 0.91 (95% CI: 0.86–0.93) and 0.91 (95% CI: 0.87–0.94), respectively. In all instances, the observed estimates of PPVs and NPVs were at least 0.82 and 0.90, respectively. The accuracy of the algorithm was not affected by different covariates (including respiratory or valvular conditions). Conclusions: This study demonstrates the efficacy of a contact-free optical device for detecting lung congestion. Further validation of the study results across a larger and precise scale is warranted.
Flexible protein alignment and hinge detection Shatsky, Maxim; Nussinov, Ruth; Wolfson, Haim J.
Proteins, structure, function, and bioinformatics,
1 August 2002, Letnik:
48, Številka:
2
Journal Article
Conservation of the spatial binding organizations at the level of physico-chemical interactions is important for the formation and stability of protein-protein complexes as well as protein and drug ...design. Due to the lack of computational tools for recognition of spatial patterns of interactions shared by a set of protein-protein complexes, the conservation of such interactions has not been addressed previously.
We performed extensive spatial comparisons of physico-chemical interactions common to different types of protein-protein complexes. We observed that 80% of these interactions correspond to known hot spots. Moreover, we show that spatially conserved interactions allow prediction of hot spots with a success rate higher than obtained by methods based on sequence or backbone similarity. Detection of spatially conserved interaction patterns was performed by our novel MAPPIS algorithm. MAPPIS performs multiple alignments of the physico-chemical interactions and the binding properties in three dimensional space. It is independent of the overall similarity in the protein sequences, folds or amino acid identities. We present examples of interactions shared between complexes of colicins with immunity proteins, serine proteases with inhibitors and T-cell receptors with superantigens. We unravel previously overlooked similarities, such as the interactions shared by the structurally different RNase-inhibitor families.
The key contribution of MAPPIS is in discovering the 3D patterns of physico-chemical interactions. The detected patterns describe the conserved binding organizations that involve energetically important hot spot residues and are crucial for the protein-protein associations.
Prediction of protein–RNA interactions at the atomic level of detail is crucial for our ability to understand and interfere with processes such as gene expression and regulation. Here, we investigate ...protein binding pockets that accommodate extruded nucleotides not involved in RNA base pairing. We observed that most of the protein-interacting nucleotides are part of a consecutive fragment of at least two nucleotides whose rings have significant interactions with the protein. Many of these share the same protein binding cavity and more than 30% of such pairs are π-stacked. Since these local geometries cannot be inferred from the nucleotide identities, we present a novel framework for their prediction from the properties of protein binding sites.
First, we present a classification of known RNA nucleotide and dinucleotide protein binding sites and identify the common types of shared 3-D physicochemical binding patterns. These are recognized by a new classification methodology that is based on spatial multiple alignment. The shared patterns reveal novel similarities between dinucleotide binding sites of proteins with different overall sequences, folds and functions. Given a protein structure, we use these patterns for the prediction of its RNA dinucleotide binding sites. Based on the binding modes of these nucleotides, we further predict an RNA fragment that interacts with those protein binding sites. With these knowledge-based predictions, we construct an RNA fragment that can have a previously unknown sequence and structure. In addition, we provide a drug design application in which the database of all known small-molecule binding sites is searched for regions similar to nucleotide and dinucleotide binding patterns, suggesting new fragments and scaffolds that can target them.
Recognition of binding patterns common to a set of protein structures is important for recognition of function, prediction of binding, and drug design. We consider protein binding sites represented ...by a set of 3D points with assigned physico-chemical and geometrical properties important for protein-ligand interactions. We formulate the multiple binding site alignment problem as detection of the largest common set of such 3D points. We discuss the computational problem of multiple common point set detection and, particularly, the matching problem in K-partite-epsilon graphs, where K partitions are associated with K structures and edges are defined between epsilon-close points. We show that the K-partite-epsilon matching problem is NP-hard in the Euclidean space with dimension larger than one. Consequently, we show that the largest common point set problem between three point sets is NP-hard. On the practical side, we present a novel computational method, MultiBind, for recognition of binding patterns common to a set of protein structures. It performs a multiple alignment between protein binding sites in the absence of overall sequence, fold, or binding partner similarity. Despite the NP-hardness results, in our applications, we practically overcome the exponential number of multiple alignment combinations by applying an efficient branchand- bound filtering procedure. We show applications of MultiBind to several biological targets. The method recognizes patterns which are responsible for binding small molecules, such as estradiol, ATP/ANP, and transition state analogues.
An unbiased survey has been made of the stable, most abundant multi-protein complexes in Desulfovibrio vulgaris Hildenborough (DvH) that are larger than Mr almost equal to 400 k. The quaternary ...structures for 8 of the 16 complexes purified during this work were determined by single-particle reconstruction of negatively stained specimens, a success rate almost equal to10 times greater than that of previous "proteomic" screens. In addition, the subunit compositions and stoichiometries of the remaining complexes were determined by biochemical methods. Our data show that the structures of only two of these large complexes, out of the 13 in this set that have recognizable functions, can be modeled with confidence based on the structures of known homologs. These results indicate that there is significantly greater variability in the way that homologous prokaryotic macromolecular complexes are assembled than has generally been appreciated. As a consequence, we suggest that relying solely on previously determined quaternary structures for homologous proteins may not be sufficient to properly understand their role in another cell of interest.
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