Filip Jagodzinski

KINARI-Web: a server for protein rigidity analysis

KINARI-Web is an interactive web server for performing rigidity analysis and visually exploring rigidity properties of proteins. It also provides tools for pre-processing the input data, such as selecting relevant chains from PDB files, adding hydrogen atoms and identifying stabilizing interactions. KINARI-Web offers a quick-start option for beginners, and highly customizable features for the experienced user. Chains, residues or atoms, as well as stabilizing constraints can be selected, removed or added, and the user can designate how different chemical interactions should be modeled during rigidity analysis. The enhanced Jmol-based visualizer allows for zooming in, highlighting or investigating different calculated rigidity properties of a molecular structure. KINARI-Web is freely available at http://kinari.cs.umass.edu.

The XML log standard for digital libraries: analysis, evolution, and deployment

We describe current efforts and developments building on our proposal for an XML log standard format for digital library (DL) logging analysis and companion tools. Focus is given to the evolution of formats and tools, based on analysis of deployment in several DL systems and testbeds. Recent development of analysis tools also is discussed.

Using rigidity analysis to probe mutation-induced structural changes in proteins.

Predicting how a single amino acid substitution affects the stability of a protein structure is a fundamental task in macromolecular modeling. It has relevance to drug design and understanding of disease-causing protein variants. We present KINARI-Mutagen, a web server for performing in silico mutation experiments on protein structures from the Protein Data Bank. Our rigidity-based approach permits fast evaluation of the effects of mutations that may not be easy to perform in vitro, because it is not always possible to express a protein with a specific amino acid substitution. In two case studies we use KINARI-Mutagen to identify exposed residues that are known to be conserved, and we show that our prediction in the change in a proteins stability due to a mutation of an amino acid to glycine can be correlated against experimentally derived stability data. KINARI-Mutagen is available at http://kinari.cs.umass.edu.

An Evolutionary Conservation & Rigidity Analysis Machine Learning Approach for Detecting Critical Protein Residues

In proteins, certain amino acids may play a critical role in determining their structure and function. Examples include flexible regions which allow domain motions, and highly conserved residues on functional interfaces which play a role in binding and interaction with other proteins. Detecting these regions facilitates the analysis and simulation of protein rigidity and conformational changes, and aids in characterizing protein-protein binding. We present a machine-learning based method for the analysis and prediction of critical residues in proteins. We combine amino-acid specific information and data obtained by two complementary methods. One method, KINARI-Mutagen, performs graph-based analysis to find rigid clusters of amino acids in a protein, and the other method uses evolutionary conservation scores to find functional interfaces in proteins. We devised a machine learning model that combines both methods, in addition to amino acid type and solvent accessible surface area, to a dataset of proteins with experimentally known critical residues, and were able to achieve over 77% prediction rate, more than either of the methods separately.

Periodic rigidity of protein crystal structures

We initiate in silico rigidity-theoretical studies of protein crystal structures, with the goal to determine if, and how, the interactions among neighboring crystal cells affect the flexibility of biological unit. We use an efficient graph-based algorithm for rigidity analysis, and other tools available through the KINARI-Web server developed in our group. For the RNase A protein (PDB file 5RSA), which has the remarkable property of being functionally active even when crystallized, we found that the individual protein and its crystal form retain the flexibility parameters between the two states. By contrast, other proteins in our data set aggregated in larger rigid clusters when analyzed as crystals.

ProMuteHT: A High Throughput Compute Pipeline for Generating Protein Mutants in silico

Understanding how an amino acid substitution affects a proteins structure is fundamental to advancing drug design and protein docking studies. Mutagenesis experiments on physical proteins provide a precise assessment of the effects of mutations, but they are time and cost prohibitive. Computational approaches for performing in silico amino acid substitutions are available, but they are not suited for generating large numbers of protein variants needed for high-throughput screening studies. We present ProMuteHT, a program for high throughput in silico generating user-specified sets of mutant protein structures with single or multiple amino acid substitutions. We combine our custom mutation algorithm with side chain homology modeling external libraries, and generate energetically feasible mutant structures. Our efficient command-line invocation syntax requires only a few arguments to specify large datasets of mutant structures. We achieve quick run-times due to our hybrid approach in which we limit the use of costly energy calculations when mutating from a large to a small amino acid. We compare our mutant structures with those generated by FoldX, and report faster run-times. We show that the mutants generated by ProMuteHT are of high quality, as determined via all-atom and mutated residue RMSD measurements for existing mutant structures in the PDB.

Rigidity analysis of protein biological assemblies and periodic crystal structures.

BackgroundWe initiate in silico rigidity-theoretical studies of biological assemblies and small crystals for protein structures. The goal is to determine if, and how, the interactions among neighboring cells and subchains affect the flexibility of a molecule in its crystallized state. We use experimental X-ray crystallography data from the Protein Data Bank (PDB). The analysis relies on an effcient graph-based algorithm. Computational experiments were performed using new protein rigidity analysis tools available in the new release of our KINARI-Web server http://kinari.cs.umass.edu.ResultsWe provide two types of results: on biological assemblies and on crystals. We found that when only isolated subchains are considered, structural and functional information may be missed. Indeed, the rigidity of biological assemblies is sometimes dependent on the count and placement of hydrogen bonds and other interactions among the individual subchains of the biological unit. Similarly, the rigidity of small crystals may be affected by the interactions between atoms belonging to different unit cells.We have analyzed a dataset of approximately 300 proteins, from which we generated 982 crystals (some of which are biological assemblies). We identified two types of behaviors. (a) Some crystals and/or biological assemblies will aggregate into rigid bodies that span multiple unit cells/asymmetric units. Some of them create substantially larger rigid cluster in the crystal/biological assembly form, while in other cases, the aggregation has a smaller effect just at the interface between the units. (b) In other cases, the rigidity properties of the asymmetric units are retained, because the rigid bodies did not combine.We also identified two interesting cases where rigidity analysis may be correlated with the functional behavior of the protein. This type of information, identified here for the first time, depends critically on the ability to create crystals and biological assemblies, and would not have been observed only from the asymmetric unit.For the Ribonuclease A protein (PDB file 5RSA), which is functionally active in the crystallized form, we found that the individual protein and its crystal form retain the flexibility parameters between the two states. In contrast, a derivative of Ribonuclease A (PDB file 9RSA), has no functional activity, and the protein in both the asymmetric and crystalline forms, is very rigid.For the vaccinia virus D13 scaffolding protein (PDB file 3SAQ), which has two biological assemblies, we observed a striking asymmetry in the rigidity cluster decomposition of one of them, which seems implausible, given its symmetry. Upon careful investigation, we tracked the cause to a placement decision by the Reduce software concerning the hydrogen atoms, thus affecting the distribution of certain hydrogen bonds. The surprising result is that the presence or lack of a very few, but critical, hydrogen bonds, can drastically affect the rigid cluster decomposition of the biological assembly.ConclusionThe rigidity analysis of a single asymmetric unit may not accurately reflect the proteins behavior in the tightly packed crystal environment. Using our KINARI software, we demonstrated that additional functional and rigidity information can be gained by analyzing a proteins biological assembly and/or crystal structure. However, performing a larger scale study would be computationally expensive (due to the size of the molecules involved). Overcoming this limitation will require novel mathematical and computational extensions to our software.

Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability

Predicting how a point mutation alters a protein’s stability can guide pharmaceutical drug design initiatives which aim to counter the effects of serious diseases. Conducting mutagenesis studies in physical proteins can give insights about the effects of amino acid substitutions, but such wet-lab work is prohibitive due to the time as well as financial resources needed to assess the effect of even a single amino acid substitution. Computational methods for predicting the effects of a mutation on a protein structure can complement wet-lab work, and varying approaches are available with promising accuracy rates. In this work we compare and assess the utility of several machine learning methods and their ability to predict the effects of single and double mutations. We in silico generate mutant protein structures, and compute several rigidity metrics for each of them. We use these as features for our Support Vector Regression (SVR), Random Forest (RF), and Deep Neural Network (DNN) methods. We validate the predictions of our in silico mutations against experimental ΔΔG stability data, and attain Pearson Correlation values upwards of 0.71 for single mutations, and 0.81 for double mutations. We perform ablation studies to assess which features contribute most to a model’s success, and also introduce a voting scheme to synthesize a single prediction from the individual predictions of the three models.

Assessing how multiple mutations affect protein stability using rigid cluster size distributions

Predicting how amino acid substitutions affect the stability of a protein has relevance to drug design and may help elucidate the mechanisms of disease-causing protein variants. Unfortunately, wet-lab experiments are time intensive, and to the best of our knowledge there are no efficient computational techniques to asses the effect of multiple mutations. In this work we present a new approach for inferring the effects of single and multiple mutations on a proteins structure. Our rMutant algorithm generates in silico mutants with single or multiple amino acid substitutions. We use a graph-theoretic rigidity analysis approach to compute the distributions of rigid cluster sizes of the wild type and mutant structures which we then analyze to infer the effect of the amino acid substitutions. We successfully predict the effects of multiple mutations for which our previous methods were unsuccessful. We validate the predictions of our computational approach against experimental ΔΔG data. To demonstrate the utility of using rigid cluster size distributions to infer the effects of mutations, we also present a Random Forest Machine Learning approach that relies on rigidity data to predict which residues are critical to the stability of a protein. We predict the destabilizing effects of a single or multiple mutations with over 86% accuracy.

Rigidity and flexibility of protein-nucleic acid complexes

The study of protein-nucleic acid complexes is relevant for the understanding of many biological processes, including transcription, translation, replication, and recombination. The individual molecules in such complexes must be rigid enough to allow geometric matching of complementary shapes, yet sufficiently flexible to perform their functions. In this paper, we present a newly developed extension to KINARI-Web, our freely available server for biomolecular rigidity analysis, to permit the analysis of PDB files containing nucleic acids and protein-nucleic acid complexes. Previously, only the protein portion of these complexes could be analyzed by KINARI. To the best of our knowledge, no other publicly available rigidity analysis software has this capability. We demonstrate this new feature by performing in silico rigidity studies on two data sets of protein-nucleic acid complexes, both in the absence and presence of nucleic acids. We find that the inclusion of nucleic acids significantly alters the rigidity of 40% of the 506 structures we analyzed.