Kamal Al Nasr

A Machine Learning Approach for the Identification of Protein Secondary Structure Elements from Electron Cryo-Microscopy Density Maps†

The accuracy of the secondary structure element (SSE) identification from volumetric protein density maps is critical for de-novo backbone structure derivation in electron cryo-microscopy (cryoEM). It is still challenging to detect the SSE automatically and accurately from the density maps at medium resolutions (∼5-10 Å). We present a machine learning approach, SSELearner, to automatically identify helices and β-sheets by using the knowledge from existing volumetric maps in the Electron Microscopy Data Bank. We tested our approach using 10 simulated density maps. The averaged specificity and sensitivity for the helix detection are 94.9% and 95.8%, respectively, and those for the β-sheet detection are 86.7% and 96.4%, respectively. We have developed a secondary structure annotator, SSID, to predict the helices and β-strands from the backbone Cα trace. With the help of SSID, we tested our SSELearner using 13 experimentally derived cryo-EM density maps. The machine learning approach shows the specificity and sensitivity of 91.8% and 74.5%, respectively, for the helix detection and 85.2% and 86.5% respectively for the β-sheet detection in cryoEM maps of Electron Microscopy Data Bank. The reduced detection accuracy reveals the challenges in SSE detection when the cryoEM maps are used instead of the simulated maps. Our results suggest that it is effective to use one cryoEM map for learning to detect the SSE in another cryoEM map of similar quality.

Solving the secondary structure matching problem in cryo-EM de novo modeling using a constrained K-shortest path graph algorithm

Electron cryomicroscopy is becoming a major experimental technique in solving the structures of large molecular assemblies. More and more three-dimensional images have been obtained at the medium resolutions between 5 and 10 Å. At this resolution range, major α-helices can be detected as cylindrical sticks and β-sheets can be detected as plain-like regions. A critical question in de novo modeling from cryo-EM images is to determine the match between the detected secondary structures from the image and those on the protein sequence. We formulate this matching problem into a constrained graph problem and present an O(Δ2N22N) algorithm to this NP-Hard problem. The algorithm incorporates the dynamic programming approach into a constrained K-shortest path algorithm. Our method, DP-TOSS, has been tested using α-proteins with maximum 33 helices and α-β proteins up to five helices and 12 β-strands. The correct match was ranked within the top 35 for 19 of the 20 α-proteins and all nine α-β proteins tested. The results demonstrate that DP-TOSS improves accuracy, time and memory space in deriving the topologies of the secondary structure elements for proteins with a large number of secondary structures and a complex skeleton.

Structure prediction for the helical skeletons detected from the low resolution protein density map

BackgroundThe current advances in electron cryo-microscopy technique have made it possible to obtain protein density maps at about 6-10 Å resolution. Although it is hard to derive the protein chain directly from such a low resolution map, the location of the secondary structures such as helices and strands can be computationally detected. It has been demonstrated that such low-resolution map can be used during the protein structure prediction process to enhance the structure prediction.ResultsWe have developed an approach to predict the 3-dimensional structure for the helical skeletons that can be detected from the low resolution protein density map. This approach does not require the construction of the entire chain and distinguishes the structures based on the conformation of the helices. A test with 35 low resolution density maps shows that the highest ranked structure with the correct topology can be found within the top 1% of the list ranked by the effective energy formed by the helices.ConclusionThe results in this paper suggest that it is possible to eliminate the great majority of the bad conformations of the helices even without the construction of the entire chain of the protein. For many proteins, the effective contact energy formed by the secondary structures alone can distinguish a small set of likely structures from the pool.

Building the initial chain of the proteins through de novo modeling of the cryo-electron microscopy volume data at the medium resolutions

Cryo-electron Microscopy (cryoEM) is an advanced imaging technique that produces volume maps at different resolutions. This technique is capable of visualizing large molecular complexes such as viruses and ribosomes. At the medium resolutions, such as 5 to 10Å, the location and orientation of the secondary structure elements (SSEs) can be computationally identified. However, there is no registration between the detected SSEs and the protein sequence, and therefore it is challenging to derive the atomic structure from such volume data. We present, in this paper, the preliminary results of the full-atom protein chains using our de novo modeling framework. The framework has multiple components including the ranking of topologies, the construction of helices and loops along the density traces, and the energy evaluation of the structure. A test containing thirteen simulated density maps and two experimentally derived density maps show that the true topology was ranked among the top 35 of the huge topological space. The best atomic model of the true topology was ranked within the top 40 for twelve of the fifteen proteins tested. The average backbone RMSD100 of these models is about 4Å for the fifteen proteins.

Estimating Loop Length from CryoEM Images at Medium Resolutions

BackgroundDe novo protein modeling approaches utilize 3-dimensional (3D) images derived from electron cryomicroscopy (CryoEM) experiments. The skeleton connecting two secondary structures such as α-helices represent the loop in the 3D image. The accuracy of the skeleton and of the detected secondary structures are critical in De novo modeling. It is important to measure the length along the skeleton accurately since the length can be used as a constraint in modeling the protein.ResultsWe have developed a novel computational geometric approach to derive a simplified curve in order to estimate the loop length along the skeleton. The method was tested using fifty simulated density images of helix-loop-helix segments of atomic structures and eighteen experimentally derived density data from Electron Microscopy Data Bank (EMDB). The test using simulated density maps shows that it is possible to estimate within 0.5Å of the expected length for 48 of the 50 cases. The experiments, involving eighteen experimentally derived CryoEM images, show that twelve cases have error within 2Å.ConclusionsThe tests using both simulated and experimentally derived images show that it is possible for our proposed method to estimate the loop length along the skeleton if the secondary structure elements, such as α-helices, can be detected accurately, and there is a continuous skeleton linking the α-helices.

An Effective Computational Method Incorporating Multiple Secondary Structure Predictions in Topology Determination for Cryo-EM Images

A key idea in de novo modeling of a medium-resolution density image obtained from cryo-electron microscopy is to compute the optimal mapping between the secondary structure traces observed in the density image and those predicted on the protein sequence. When secondary structures are not determined precisely, either from the image or from the amino acid sequence of the protein, the computational problem becomes more complex. We present an efficient method that addresses the secondary structure placement problem in presence of multiple secondary structure predictions and computes the optimal mapping. We tested the method using 12 simulated images from α-proteins and two Cryo-EM images of α-β proteins. We observed that the rank of the true topologies is consistently improved by using multiple secondary structure predictions instead of a single prediction. The results show that the algorithm is robust and works well even when errors/misses in the predicted secondary structures are present in the image or the sequence. The results also show that the algorithm is efficient and is able to handle proteins with as many as 33 helices.

An efficient method for validating protein models using electron microscopy data

Cryo-Electron Microscopy is a powerful biophysical technique that is capable of generating 3-dimensional volume images for macromolecular assemblies and machines. De novo protein modeling uses these images to model the biological molecules. In de novo modeling, many candidate structures are generated at intermediate step. The candidates are evaluated conventionally by time-consuming approaches. We introduce an initial version of a geometrical screening method that uses the skeleton of the cryo-EM images to evaluate the candidate structures. A test of ten (10) proteins shows that our method was able to successfully detect good candidates in an efficient way.

CryoEM skeleton length estimation using a decimated curve

Cryo-electron Microscopy (cryoEM) is an important biophysical technique that produces 3-dimensional (3D) images at different resolutions. De novo modeling is becoming a promising approach to derive the atomic structure of proteins from the cryoEM 3D images at medium resolutions. Distance measurement along a thin skeleton in the 3D image is an important step in de novo modeling. In spite of the need of such measurement, little has been investigated about the accuracy of the measurement in searching for an effective method. We propose a new computational geometric approach to estimate the distance along the skeleton. Our preliminary test results show that the method was able to estimate fairly well in eleven cases.

A Constraint Dynamic Graph Approach to Identify the Secondary Structure Topology from cryoEM Density Data in Presence of Errors

The determination of the secondary structure topology is a critical step in deriving the atomic structure from the protein density map obtained from electron cryo-microscopy technique. This step often relies on the matching of two sources of information. One source comes from the secondary structures detected from the protein density map at the medium resolution, such as 5-10 Å. The other source comes from the predicted secondary structures from the amino acid sequence. Due to the uncertainty in either source of information, a pool of possible secondary structure positions has to be sampled in order to include the true answer. A naïve way to find the native topology is to exhaustively map the pool of possible secondary structures detected in the density map with the pool of the secondary structures predicted from the sequence and search for the topology with the lowest cost. This paper studies the question that is how to reduce the computation of the mapping when the uncertainty of the secondary structure predictions is considered. We present a method that combines the concept of dynamic graph with our previous work of using constrained shortest path to identify the topology of the secondary structures. We show a reduction of about 34.55% time as comparison to the naïve way of handling the inaccuracies. To our knowledge, this is the 1st computationally effective exact algorithm to identify the optimal topology of the secondary structures when the inaccuracy of the predicted data is considered.

Analytical Approaches to Improve Accuracy in Solving the Protein Topology Problem

To take advantage of recent advances in genomics and proteomics it is critical that the three-dimensional physical structure of biological macromolecules be determined. Cryo-Electron Microscopy (cryo-EM) is a promising and improving method for obtaining this data, however resolution is often not sufficient to directly determine the atomic scale structure. Despite this, information for secondary structure locations is detectable. De novo modeling is a computational approach to modeling these macromolecular structures based on cryo-EM derived data. During de novo modeling a mapping between detected secondary structures and the underlying amino acid sequence must be identified. DP-TOSS (Dynamic Programming for determining the Topology Of Secondary Structures) is one tool that attempts to automate the creation of this mapping. By treating the correspondence between the detected structures and the structures predicted from sequence data as a constraint graph problem DP-TOSS achieved good accuracy in its original iteration. In this paper, we propose modifications to the scoring methodology of DP-TOSS to improve its accuracy. Three scoring schemes were applied to DP-TOSS and tested: (i) a skeleton-based scoring function; (ii) a geometry-based analytical function; and (iii) a multi-well potential energy-based function. A test of 25 proteins shows that a combination of these schemes can improve the performance of DP-TOSS to solve the topology determination problem for macromolecule proteins.